Thesis Related to Supply Chain Management

SUPPLY CHAIN MANAGEMENT IN A PACKING COMPANY 

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Thesis Related to Supply Chain Management,

Andreas Hammer

 Enabling Successful Supply Chain Management – Coordination, Collaboration, and
Integration for Competitive Advantage


Enabling Successful Coordination, for Competitive


May, 2006
This publication is based on a dissertation submitted to the University of Mannheim.
All rights reserved
Mannheim University Press
Mannheim University Press publishing association is a cooperation of SUMMACUM GmbH
and the Universitätsbibliothek Mannheim.
Cover design by SUMMACUM GmbH
Printed and bound by ABT Mediengruppe, Weinheim
Further information about the companies is available at
www.summacum.com and www.abt-medien.de
ISBN 3-939352-04-7
ISBN 978-3-939352-04-4

Doctoral Thesis

In fulfillment of the requirements
for the Doctor of Economics
at the University of Mannheim

Submitted by
Dipl.-Kfm. Andreas Hammer
from Pforzheim


Acknowledgements

It has been a long and exciting journey that has led to the publication of this book
entitled Enabling Successful Supply Chain Management – Coordination,
Collaboration, and Integration for Competitive Advantage. I feel very fortunate,
happy and proud about this book because it not only means that I have mastered
all the hurdles on the way to its publication but also because I think its insights are
highly valuable for companies and individuals who face the necessity of managing
not only their own business operations but increasingly also that of their supply
chain. Besides the presented empirical evidence, to me its key contributions lie in
the organization and definition of strategic supply chain management, the
presentation of the most important issues surrounding it and in the suggestions to
address and to resolve some of them.

Of course, I am indebted to many people who contributed to the success of this
endeavour. First of all, I want to thank Prof. Dr. Peter Milling and Prof. Dr. Frank
Maier for accompanying and supporting my dissertation project at the University
of Mannheim. Prof. Dr. Frank Maier especially initiated my desire to take on this
challenge and motivated me throughout this time through his continuous belief in
my ability to pull off this dissertation. He also provided me with a great working
place as Research and Teaching Associate in his department at the International
University in Germany, Bruchsal (IU).

The working environment at IU significantly contributed to the
overwhelmingly positive experience throughout my dissertation time from
September 2002 until February 2006. Personally, I grew a lot through the
opportunity to lead my own courses, through working with our students on
interesting topics and cases and through many international conference
participations. With regard to these conferences (and also my seminar and course
work time at the University of Mannheim), I want to especially thank Dipl.-Kfm.
Johannes von Mikulicz-Radecki and Dipl.-Wirtsch.-Ing. Jan Jürging for a great
time. True, I still play by far the worst golf of the three of us, but at least I have
managed to finish the dissertation first.

My colleagues during my time at IU were simply awesome and I want to
especially thank my fellow research and teaching associates at the School of
Business Administration and Prof. Dr. Jan Doppegieter, who were all great
company not only in our coffee club but also at other extracurricular activities.
Another friend I want to especially point out is Prof. Dr. Robert Marble, whom I
have known since my graduate studies at the University of Mannheim and got to
know more intensively during my studies abroad at Creighton University in
Omaha. He also joined IU for half a year as guest professor. I want to thank him


for all his support and the great time we have had so far. He also gave valuable
input for this dissertation. Many thanks also go to Frank Funicello for proofreading
this dissertation and IU's librarian Michaela Glaum, who managed to
provide me with even the most exotic articles often within hours of requesting
them.

Last but not least, I want to thank my mother for always helping me out when
needed, my father for supporting this endeavour in every way, my brother as my
closest relative for being the buddy he is, and my girlfriend Tanja for her great
understanding and endless support. This book is dedicated to them.

Andreas Hammer
Munich, August 2006

“Science is facts; just as houses are made of stones, so is science made of
facts; but a pile of stones is not a house and a collection of facts is not
necessarily science.”

Henri Poincaré, French mathematician & physicist


Table of Contents

Acknowledgements............................................................................................. V


Table of Contents..............................................................................................VII


List of Tables ..................................................................................................... XI


List of Figures................................................................................................. XIII


Abbreviations....................................................................................................XV


A.
A Shift to Supply Chain Competition – Moving Beyond Management
Science.......................................................................................................... 1

B.
Improving Supply Chain Performance through E-Business Enabled
Supply Chain Management........................................................................... 7

I.
Supply Chain Management as a Strategic Option for Companies......... 7


1. Emergence of the Concept and Definitions......................................... 7


2. Supply Chain Management as a Perspective for Inter-Company
Management...................................................................................... 20

3. Concepts of Supply Chain Management ........................................... 22


3.a.
Supply Chain Management Objectives and Strategic
Relevance ................................................................................ 22

3.b.
Elements of Supply Chain Management and Underlying
Theories ................................................................................... 33

3.c.
A Third Generation Model for Supply Chain Management .... 41

II.
Cooperation as Success Factor for Supply Chain Management .......... 48


1. The Bullwhip Effect as a Result of Uncoordinated Decision
Making .............................................................................................. 48

2. Supply Chain Management Cooperation: Coordination,
Collaboration, and Integration at the Core ........................................ 52

3. Importance of Information for Coordination, Collaboration and
Integration in Supply Chains............................................................. 65

III. Holistic View of Supply Chain Management ...................................... 72


1. Role of Strategic Fit in Supply Chains.............................................. 72


2. Processes and Structures in Supply Chains........................................76



Table of Contents

2.a.
Adopting a Process View of Supply Chain Operations............76


2.b.
Connecting Supply Chain Management Processes with the
Supply Chain Operations Reference (SCOR) Model ...............80

2.c.
A Formal Framework for Supply Chain Structures..................86


3. E-Business Enabled Supply Chain Management...............................97


3.a.
From Information and Communication Technology to EBusiness....................................................................................
97

3.b.
A Business View of E-Business.............................................104


3.c.
E-Business as a Catalyst for Supply Chain Management.......108


3.d.
Applications and Developments of E-Business in Supply
Chain Management ................................................................113

IV. Challenges for Supply Chain Management ........................................123


1. Involvement in Multiple Supply Chains ..........................................123


2. Power Regimes in Supply Chains....................................................126


3. A Note on Vertical Integration and Uncertainties............................132


C.
Supply Chain Management and E-Business in Manufacturing
Companies – a Descriptive Analysis of Practices .....................................137

I.
Overview of the High Performance Manufacturing (HPM) Project...137

II.
Role of Manufacturing Companies in Supply Chains ........................141


1. Positions of HPM Plants in Their Supply Chains ............................141


2. Value Creation of Manufacturing Companies .................................146


3. Supply Chain Management Practices and Performance –
Empirical Evidence..........................................................................156

3.a.
Operationalizing Supply Chain Management.........................156


3.b.
Determination and Validity of SCM Practice Clusters...........158


3.c.
Performance Implications of Supply Chain Management
Practices .................................................................................162

III. E-Business Applications and Practices of Manufacturing
Companies..........................................................................................169

1. E-Business Usage in Manufacturing Companies.............................169


2. Electronic Integration of Business Partners in Manufacturing
Supply Chains..................................................................................176

3. Evidence for the Impact of Software Support and ERP Integration
on Manufacturing Supply Chains ....................................................179

IV. Evidence for Superior Performance of SCM Integration Champions 185


D.
Analysis of the Supply Chain Management Framework .......................... 193


I.
Path Model Estimation Using Partial Least Squares (PLS) ............... 193


1. Partial Least Squares as a Causal Modeling Technique .................. 194


2. Comparison of Partial Least Squares with Linear Structural
Relation (LISREL) Modeling.......................................................... 200

3. Methodological Application and Interpretation of the Partial Least
Squares Method............................................................................... 204

II.
Supply Chain Management, E-Business Factors, and Performance
for the PLS Analysis.......................................................................... 208

III. PLS Analysis of the Supply Chain Management Framework............ 213


1. Path Model Estimation of Supply Chain Management Framework 213

2. Quality, Validity, and Reliability of PLS Model Results ................ 216


3. Insights from and Limitations of the Empirical Investigation of the
Supply Chain Management Framework.......................................... 220

E.
Theoretical and Practical Implications for Successful Supply Chain
Management ............................................................................................. 227

References........................................................................................................ 231


Appendices....................................................................................................... 255



List of Tables

Table B-1: Selected, recent SCM definitions, sorted by year......................... 18
Table B-2: Identified tactical SCM objective items ....................................... 29
Table B-3: Identified operational SCM objectives ......................................... 31
Table B-4: Hierarchical structure of the SCOR model................................... 81
Table B-5: Complicating matters of multiple supply chains within a


company and their implications.................................................. 124


Table C-1: Structure of data sample of the second round of the HPM

project......................................................................................... 140
Table C-2: Number of employees in the HPM project dataset, according

to industry and in total................................................................ 140
Table C-3: Discriminant goodness measures for customer segment

clusters........................................................................................ 143
Table C-4: Classification results for customer segment clusters .................. 144
Table C-5: Clusters according to customer segments................................... 145
Table C-6: Cluster allocation by industry based on customer segment

clusters........................................................................................ 146
Table C-7: Mean and median for total added value per plant for each

cluster ......................................................................................... 147


Table C-8: Added value in manufacturing function of plants in the HPM

project by industry, in percentage of total manufacturing

costs............................................................................................ 150


Table C-9: Added value in manufacturing function of plants according

to customer segment structure .................................................... 151
Table C-10: Perceived vertical supply chain integration of the sample.......... 152
Table C-11: Perceived vertical supply chain integration by customer

segment cluster ........................................................................... 152
Table C-12: Outsourcing of selected activities of manufacturing plants........ 154
Table C-13: Percentage of purchases from home country, by industry and

in total......................................................................................... 155
Table C-14: Percentage of sales to home country, by industry and in total.... 156
Table C-15: T-values for SCM clusters.......................................................... 160
Table C-16: F-values for SCM clusters .......................................................... 161
Table C-17: Discriminant goodness measures for SCM practice clusters...... 162


List of Tables

Table C-18: Classification results for SCM practice clusters..........................162
Table C-19: Informants of measurement constructs........................................165
Table C-20: Performance comparison for different degrees of SCM

practices ......................................................................................168
Table C-21: Software support of selected SCM applications by country........170
Table C-22: Usage of the Internet for procurement activities by country

and in total...................................................................................173
Table C-23: Usage of the Internet for sales activities by country and in

total .............................................................................................174
Table C-24: Usage of the Internet for sales activities by cluster and over

all clusters ...................................................................................174
Table C-25: Usage of the Internet for procurement activities by cluster

and over all clusters.....................................................................175
Table C-26: Cluster analysis based on Internet adoption for purchasing

and sales processes, means of clusters ........................................178
Table C-27: Comparison of selected performance measures for low and

high software adopters ................................................................182
Table C-28: Comparison of selected performance measures for overall

low and high IT adopters.............................................................184
Table C-29: Customer satisfaction for plants with low and high ERP

integration ...................................................................................185
Table C-30: Performance overview SCM integration groups .........................189


Table D-1: Comparison of PLS and covariance-based SEMs (e.g.,

LISREL)......................................................................................203
Table D-2: Overview of reliability of reflective scales used in the causal

model...........................................................................................212
Table D-3: Path coefficients of initial model estimation...............................213
Table D-4: Path coefficients of structural equation model ............................216
Table D-5: Composite reliability, Cronbach’s alpha, and average

variance explained for reflective scales.......................................217
Table D-6: Assessing discriminant validity...................................................218
Table D-7: f 2 values for dependent latent variables......................................219
Table D-8: q2 values for all independent latent variables..............................220



List of Figures

Figure B-1: Anglo-American vs. Continental-European understanding of

logistics and SCM......................................................................... 13
Figure B-2: Efficient asset and operating frontiers .......................................... 24
Figure B-3: Effect of improvement and betterment on the efficient

performance frontier..................................................................... 25
Figure B-4: A third dimension for performance frontiers................................ 26
Figure B-5: Fundamental operational SCM components................................. 35
Figure B-6: A holistic third generation SCM framework ................................ 43
Figure B-7: Overview of SCM theory discussion structure............................. 47
Figure B-8: Different views on SCM processes .............................................. 79
Figure B-9: Overview of all SCOR process categories, according to the


SCOR model................................................................................. 82
Figure B-10: Simplified supply chain network structure ................................... 87
Figure B-11: Simplified supply chain network, illustrating physical,

information, and financial flows................................................... 91
Figure B-12: Supply chain network structure, including position notions......... 93
Figure B-13: Tier depiction of a supply chain network ..................................... 95
Figure B-14: Power regimes and relationship styles........................................ 127
Figure B-15: Supply chain power regimes....................................................... 130


Figure C-1: Distribution of overhead cost portion as percentage of

manufacturing costs in the HPM sample .................................... 149
Figure C-2: Identified SCM clusters of HPM plants, based on t-values ........ 160
Figure C-3: Illustration of SCM software support and ERP integration ........ 171
Figure C-4: Reported median of IT expenses and range as percentage of

manufacturing costs by country.................................................. 172
Figure C-5: Illustration of Internet adoption of purchasing and

sales processes............................................................................ 177
Figure C-6: Portfolio classification according to SCM integration

capabilities.................................................................................. 187



XIV List of Figures

Figure D-1: Schematic PLS model. ................................................................195
Figure D-2: Hypothesized causal SCM model, based on available

HPM scales .................................................................................211
Figure D-3: Initial model path coefficients after removing insignificant links214
Figure D-4: Final PLS model configuration with best model fit for SCM

framework ...................................................................................215



Abbreviations

4PL = Fourth party logistics service provider
ARPA = Advanced research projects agency
ASCC = Automatic Sequence Controlled Calculator
ASCII = American Standard Code for Information Interchange
CAD = Computer aided design
CERN = Conseil Europeen pour la Recherche Nucleaire or European
Organization for Nuclear Research
CIM = Computer integrated manufacturing
CPFR = Collaborative planning, forecasting and replenishment
CSS = Cascading style sheets
DIKW = Data, information, knowledge and wisdom hierarchy
EAN = European Article Number
ECR = Efficient consume response
EDI = Electronic data interchange
EPC = Electronic product code
ERP = Enterprise resource planning
FTP = File transfer protocol
GMRG = Global Manufacturing Research Group
GSM = Global system of communication
HPM = High performance manufacturing
HTML = Hypertext markup language
HTTP = Hypertext transfer protocol
IETF = Internet engineering task group
ISO = International organization for standardization
IT = Information technology
JIT = Just-in-time
LISREL = Linear structural relation modeling
MIT = Massachusetts Institute of Technology
MPEG = Motion pictures expert group
MPG3 = Motion pictures expert group audio format
MRP II = Manufacturing resource planning
MRP = Material requirement planning


XVI Abbreviations
NACE = Nomenclature Generale des Activites Economiques dans I`Union
Europeenne (Classification of economic activities in the European
Community)
NAICS = North American industry classification system
NDCF = Net Discounted Cash Flows
NSF = National science foundation
OEM = Original equipment manufacturer
OASIS = Organization for the Advancement of Structured Information
Standards
PC = Personal computer
PDF = Portable document format
PIMS = Profit Impact on Marketing Strategies
PLS = Partial least squares
PRTM = Pittiglio Rabin Todd & McGrath, consulting company
RAM = Random Access Memory
RFID = Radio frequency identification
SCC = Supply chain council
SCM = Supply chain management
SCOR = Supply chain operations reference
SEM = Structural equation model(ing)
TCP/IP = Transmission control protocol / Internet protocol
TQM = Total quality management
UCC = Uniform code council
UMTS = Universal mobile telecommunications system
UPC = Universal product code
URI = Uniform resource identifier
URL = Uniform resource locator
VMI = Vendor managed inventory
W3C = World wide web consortium
WLAN = Wireless local area network
WWW = World wide web
XML = Extensible markup language


 
A. A Shift to Supply Chain Competition – Moving
Beyond Management Science
The concept of supply chain management (SCM) is not a new idea. Its meaning
has changed considerably, however, over the last decade as researchers have
identified potential benefits going along with an expansion of scope of the
concept. With this increase in scope, the company vs. company view of
competition did not seem to be adequate anymore and the notion of supply chain
vs. supply chain competition was adopted by many researchers.1

Several macro-economic developments have contributed to this recognition.
Significant factors include globalization, a change towards customer-driven
markets, acknowledgment of dependencies, a focus on core competencies,
increasing cost competition, and advances in information technology. In the
following paragraphs, these factors are briefly reviewed:

1.
Globalization. The political developments of trade policies were and are
accompanied by improvements in logistics services and communication
technology. This makes global sourcing and partnering more feasible than a
few years ago. Managing such global networks on both the supplier as well as
the customer side gained in importance as it expanded the opportunities of
competition.2 The enlarged geographical and societal scope now requires
companies to manage increased uncertainties.3
1
One of the first references can be traced back to Porter, Michael E.: The competitive
advantage of nations, New York 1990, p. 3 in the context of value systems and to
Christopher, Martin: Logistics and supply chain management, London 1992, in the
context of supply chains. This notion was picked up in the following years by many
researchers. Some of the most prominent examples can be found in Lambert, Douglas
M., Martha C. Cooper and Janus D. Pagh: Supply chain management: Implementation
issues and research opportunities, in: The International Journal of Logistics
Management, Vol. 9 (1998), No. 2, p. 1; McCormack, Kevin P. and William C.
Johnson: Supply chain networks and business process orientation: Advanced strategies
and best practices, Boca Raton 2003, p. 33. The underlying principles of this
development, however, were already laid out by Forrester in 1961, see Forrester, Jay
W.: Industrial dynamics, Waltham 1961, reprinted in 1999, pp. 4-9. This will be
followed up in more detail in section B.I.1.

2 See Christopher, Martin: Logistics and supply chain management: Creating value-
adding networks, 3rd ed., Harlow 2005, pp. 32-33.
3 See Lee, Hau L. and Seungjin Whang: Information sharing in a supply chain, in:
International Journal of Technology Management, Vol. 20 (2000), No. 3/4, p. 374.


A Shift to Supply Chain Competition

2.
Customer-driven markets. Customers, especially end customers4, are more
often offered alternatives to existing products and services. This has
empowered consumers and leads to advanced requirements concerning all
product or service characteristics. Availability of the “right product or
service” is considered to be of differentiating importance.5 Furthermore, basic
needs are overly satisfied in the major economies,6 which has led to a more
unpredictable consumer behavior and therefore higher volatility of demand.7
3.
Dependency. Companies realize that they are not operating in a vacuum. They
are dependent not only on their customers, but also increasingly on other
stakeholders, for example their suppliers, employees, authorities, and
shareholders.8
4.
Focus on core competencies. Since the early 1990s, companies have focused
more on their core competencies in order to remain competitive.9 By doing so,
they have stripped off activities that do not contribute to these competencies.
Consequently, this has led to outsourcing and vertical disintegration.10
4
The terms “end customer” and “consumer” are used in this text interchangeably. There
cannot be a “real” end customer before the consumer since all products and services
ultimately reach the consumption stage. See also Kuhn, Axel and Bernd Hellingrath:
Supply Chain Management: Optimierte Zusammenarbeit in der Wertschöpfungskette,
Berlin Heidelberg New York 2002, p. 2.

5
See Fulkerson, William: Information-based manufacturing in the informational age, in:
The International Journal of Flexible Manufacturing Systems, Vol. 12 (2000), No. 2/3,
April, p. 131.

6
See Walther, Johannes: Konzeptionelle Grundlagen des Supply Chain Managements,
in: Walther, Johannes (Ed.): Supply Chain Management, Frankfurt am Main 2001,

p. 11.
7 Cf. Christopher: Logistics and supply chain management: Creating value-adding
networks, p. 117.
8
Dependency can be related to the acknowledgement of supply chain vs. supply chain
competition, as introduced on the previous page. For an early comment on this issue,
see Emery, Frederick E. and Eric L. Trist: The causal texture of organizational
environments, in: Human Relations, Vol. 18 (1965), No. 1, January, pp. 21-32.

9
See Wernerfelt, Birger: A resource-based view of the firm, in: Strategic Management
Journal, Vol. 5 (1984), No. 2, pp. 171-180; Barney, Jay: Firm resources and sustained
competitive advantage, in: Journal of Management, Vol. 17 (1991), No. 1, March, pp.
99-120; and Stalk, George, Philip Evans and Lawrence E. Shulman: Competing on
capabilities: The new rules of corporate strategy, in: Harvard Business Review, Vol. 70
(1992), No. 2, March/April, pp. 57-69.

10
See Harland, C. M.: Supply chain management: Relationships, chains and networks, in:
British Journal of Management, Vol. 7 (1996), Special Issue, pp. S64-S66.


5.
Cost competition. Though efficient operating costs have always been
considered a qualifying factor of competitiveness,11 regardless of competitive
priorities, the price pressure has increased considerably due to lower trade
barriers, excess supply in many industry sectors, and decreasing information
asymmetry. Often, companies feel as if their internal cost saving potential is
exhausted and seek further cost savings beyond their company boundaries.12
6.
Information technology. The widespread diffusion of the Internet
accompanied by digitalization of telecommunication networks, development
of broadband transmission and increased performance of distributed
computation based on common standards has led to an acceleration of the
trends mentioned and impacts customer behavior and spending drastically.13
The expanding scope of competitive considerations to the management of
entire supply chains appears to make a prophecy come true. Jay W. Forrester
foresaw in his seminal work in 1961 the increasing importance of management
science and operations research.14 At the time, management science was in its
infancy and management was considered to be more of an art that needed
organization in order to understand the foundation on which the art is based on.
According to Forrester, “as science develops to explain, organize, and distill
experience into a more compact and usable form … [and] … grows, it provides a
new basis for further extension of the art.”15 Forrester depicts the development of
the perception of management as an art and management science in a graphic
illustration. In this illustration, the two converge until the beginning of the 21st
century and then, as science builds a solid foundation, diverge again.16 After
decades of solid management science development in functional areas, the focus
has now shifted to the management of entire supply chains. This introduces a
whole new level of complexity17, which causes new challenges for management

11
On “qualifying” factors and “order-winning” factors, see Hill, Terry: Manufacturing
strategy, London 1985, p. 44.

12
Cf. Christopher: Logistics and supply chain management: Creating value-adding
networks, pp. 33-37, and Simchi-Levi, David, Philip Kaminsky and Edith Simchi-Levi:
Designing and managing the supply chain: Concepts, strategies, and case studies, 2nd
ed., New York 2003, p. 5.

13 Cf. Fulkerson: Information-based manufacturing in the informational age, p. 134.
14 Cf. Forrester: Industrial dynamics, pp. 1-3.
15 Forrester: Industrial dynamics, p. 2.
16 Cf. Forrester: Industrial dynamics, p. 2.
17 Complexity can be defined by the dimensions (1) variety, (2) connectivity, and (3)


functionality, cf. Milling, Peter: Systemtheoretische Grundlagen zur Planung der
Unternehmenspolitik, Berlin 1981, pp. 91 et sqq., and Milling, Peter: KybernetischeÜberlegungen beim Entscheiden in komplexen Systemen, in: Milling, Peter (Ed.):
Entscheiden in komplexen Systemen, Berlin 2002, pp. 11 et sqq.


A Shift to Supply Chain Competition

science. However, developments so far build the basis for further extension of the
art. Based on existing advances in management sciences, especially the
development of advanced and easy to use research and management tools,
scientists and practitioners are now better equipped to deal with this “new”
complexity.18 The principles of systems thinking can provide the necessary tools
to develop management sciences further and to organize the field in order to
explain the art of management. It is one aim of this text to provide such
organization in the context of SCM by deriving a new framework for SCM.19

Recent developments in information technology, especially the advent of the
Internet, play a significant role in this context. In fact, it is the features of the
Internet platform, paralleled by breakthroughs in microelectronics, operating
systems, and programming languages, that represent the backbone of the above
mentioned developments.20 Kahl and Berquist identify the following key
characteristics of the Internet platform and network technology:

(1) standardization and common communication protocols, (2) ubiquity and
pervasiveness, (3) open, digital many-to-many network configuration and
infrastructure, (4) real-time communication, and (5) variety of data structures.21
By enabling individuals and organizations to communicate and exchange formal
information through a widespread, relatively cheap, and fast network, the
opportunity costs of such communication and information exchange decreases
significantly.
As these opportunity costs are important parameters for decisions on system
structures and system designs, such a change must have a profound impact on the
economic ecosystem. The current change in the underlying communication and
information technology is proceeding faster than ever before. It takes time,
however, for the economic ecosystem as well as for society to adjust. Again, not
much has changed from 40 years ago. Forrester remarked already in 1961 in
context of advances in computing power:

“Society cannot absorb so big a change in a mere ten years. We have a
tremendous untapped backlog of potential devices and applications. It is now
to be expected that machine progress will stay ahead of conceptual progress
in industrial and economic dynamics.”22

18
In line with Milling’s definition of complexity, this new complexity is created by
expanding the existing scope of management science on all dimensions, i.e. variety,
connectivity, and functionality, see Milling: Systemtheoretische Grundlagen zur
Planung der Unternehmenspolitik, pp. 91 et sqq.

19 See chapter B.I.3.c.
20 See Fulkerson: Information-based manufacturing in the informational age, p. 134.
21 See Kahl, Steven J. and Thomas P. Berquist: A primer on the internet supply chain, in:


Supply Chain Management Review (2000), September/October, pp. 42-43.
22 Forrester: Industrial dynamics, p. 19.


Managers, popular press, and researchers await the latest developments of
information technology, especially recently in mobile information and
communication technology. Still, most companies are trying to figure out how
best to apply existing technology for improving business performance. They
attempt to unify separated systems scattered throughout all areas of business and
society. With that, they are trying to capture the “untapped backlog” created by
the disruptive nature of Internet technology. Though there are further refinements
and important developments to come, the underlying characteristics of the
Internet, as described above, build the basis as these technological devices and
applications develop over time.

Just as information technology keeps inventing and introducing new devices
and applications based on technological progress, business research must adjust to
the new capabilities provided by Internet technology and the accompanying social
impact. Therefore, a need for a persuasive theory in SCM, especially as a
foundation for the effective application and implementation of information
technology, was identified by researchers.23

The aim of the analysis in this text is to contribute to the theory building
process in SCM. First, grounding on existing research, a new, comprehensive
SCM framework is developed. Then, an in-depth discussion of its elements is
presented and core elements of SCM are identified. Based on this thorough
foundation, an empirical analysis is conducted in the context of the High
Performance Manufacturing (HPM) project. In this context, the analysis focuses
on the identified core SCM elements coordination, collaboration, and integration
and additionally investigates the role of information technology (IT) and e-
business.

23
See Bechtel, Christian and Jayanth Jayaram: Supply chain management: A strategic
perspective, in: The International Journal of Logistics Management, Vol. 8 (1997), No.
1, pp. 25-26; and Chen, Injazz J. and Antony Paulraj: Towards a theory of supply chain
management: The constructs and measurements, in: Journal of Operations
Management, Vol. 22 (2004), p. 133.


B. Improving Supply Chain Performance through E-
Business Enabled Supply Chain Management
I. Supply Chain Management as a Strategic Option for Companies
1. Emergence of the Concept and Definitions
The term supply chain management24 (SCM) seems to have originated from Oliver
and Webber in the early 1980s.25 It started off as a concept that stresses mainly a
coordinated, holistic view of the various segments in the chain of supply and
demand of functional areas and the strategic importance of this. Emphasis was on
the internal supply chain and the broader view of former operational logistics
activities. Over time, researchers and practitioners picked up the term and
assigned new meanings to it.

The underlying coordinative idea is a rather old one, as several authors have
pointed out with examples from centuries ago.26 The principles of SCM were first
systematically identified by Forrester.27 Even before that, von Bertalanffy and
Boulding discussed the concept of general systems theory, which brought up the
understanding that the ”[…] behaviour of a complex system cannot be understood
completely by the segregated analysis of its constituent parts.”28 The notion

24
Hereafter, the abbreviation SCM is used throughout the text.

25
See Oliver, R. Keith and Michael D. Webber: Supply chain management: Logistics
catches up with strategy, in: Christopher, Martin (Ed.): Logistics: The strategic issues,
London 1992; and cf. Harland: Supply chain management: Relationships, chains and
networks, p. S63.

26
For example, Christopher names the building of the pyramids in ancient Egypt. He also
cites the importance of food and equipment supply in the American War of
Independence, a major factor in the defeat of the British army. Cf. Christopher:
Logistics and supply chain management: Creating value-adding networks, pp. 3-4.
Geary, Childerhouse and Towill mention the capability of the Venice arsenalotti
(master ship builders) to deliver warships every 24 hours in 1574 and the just-in-time
(JIT) methods used on a large scale during the construction of Crystal Palace in
London, cf. Geary, Steve, Paul Childerhouse and Denis R. Towill: Uncertainty and the
seamless supply chain, in: Supply Chain Management Review (2002), July/August, p.

53.
27
Cf. Forrester, Jay W.: Industrial dynamics: A major breakthrough for decision makers,
in: Harvard Business Review, Vol. 38 (1958), July/August, p. 37. In this article,
Forrester relates more to an internal perspective, though it becomes clear that the
approach reaches beyond company boundaries.

28
New, Stephen J.: The scope of supply chain management research, in: Supply Chain
Management: An International Journal, Vol. 2 (1997), No. 1, p. 16 with reference to


Improving Supply Chain Performance

subsequently evolved, mainly driven by the logistics discipline, to arrive at the
significance SCM has in today’s academic environment and business literature.

The following section gives a brief overview of general, historical
developments that are relevant to derivation of the SCM concept from the
evolutionary process that occurred. The differences between logistics management
and SCM are further examined. Then, the different current perceptions and
definitions of SCM are introduced, as there exists a variety of ideas and
understandings about SCM.

Before 1975, markets were predominantly controlled by the supplier, which
often was an original equipment manufacturer (OEM).29 Manufacturers focused
on their own operations with little or no cooperative supplier relationships.
Purchasing and procurement were perceived as a “servant for production”.
Emphasis was placed on production costs per unit with little product and process
flexibility.30 Chandra and Kumar also tag the year 1975 as the time until which
companies had focused on functional optimization and their relationships with
suppliers had been mainly adversarial.31 With the introduction of Material
Requirement Planning (MRP) in the 1970s, managers realized the impact of work
in process inventory on key performance drivers, such as cost, quality, new
product development, and delivery lead time. In the following years of the 1970s,
companies began to realize the benefits of coordinating and integrating functions
within the corporation. This was followed in the 1980s by more sophisticated and
better organized management concepts. This introduced quality initiatives as the
total quality management philosophies and ISO certification efforts,
Manufacturing Resource Planning (MRP II)32, Just-in-Time (JIT), and has

Boulding, Kenneth E: General system theory - the skeleton of science, in: Management
Science, Vol. 2 (1956). Roots can be traced back to the ideas of von Bertalanffy in the
1940s, see von Bertalanffy, Ludwig: General system theory: Foundations, development,
applications, New York 1968 for an overview.

29
Cf. Schönsleben, Paul: Integrales Logistikmanagement: Planung und Steuerung der
umfassenden Supply Chain, 4th ed., Berlin Heidelberg New York 2004, p. 79. He
marks the oil crisis as the turning point of this situation as prices rose and demand
declined.

30
Cf. Tan, Keah Choon: A framework of supply chain management literature, in:
European Journal of Purchasing & Supply Management, Vol. 7 (2001), No. 1, pp. 40


41.
31
Cf. Chandra, Charu and Sameer Kumar: Supply chain management in theory and
practice: A passing fad or a fundamental change? in: Industrial Management & Data
Systems, Vol. 100 (2000), No. 3, p. 100.

32
MRP II is an extension of MRP, including all kinds of resources and providing more
sophisticated feedback to other enterprise planning systems, including operations,
marketing, and finance, see for example Hill, Terry: Operations management, 2nd ed.,
New York 2005, p. 358.


Supply Chain Management as a Strategic Option for Companies

included immediate suppliers, and transportation and distribution specialists.33
With increasingly global competition from the 1990s on, the management of
outside links to the organization gained attention in the form of strategic alliances.
At the same time, information technology picked up speed and enterprise resource
planning (ERP), product data management, and computer integrated
manufacturing (CIM) became available.34 Suppliers were included pragmatically
in order to reduce costs and improve quality by working with fewer suppliers
more strategically, aiming to eliminate redundant tasks. This view has now been
extended and broadened, including not only suppliers, but also service providers,
and customers.35

A similar, though more “aggregated”, outline is drawn by Weber, Bacher, and
Groll with regard to logistics. They have identified the following four
development steps in logistics. First, the functional view of logistics was
predominant. The second step focused on the inter-functional coordination and
attempted to influence customer needs. In the third step, entire value creation
structures have been questioned and a flow-oriented design of processes with the
entire company has been emphasized in order to accomplish competitive
advantages. The fourth step now reaches beyond company boundaries and extends
the flow-oriented process view across supply chains, i.e. from “source of supply”
to “point of consumption”.36

According to a study conducted by Deloitte, this evolutionary process can also
be observed in the product development process of leading manufacturers. From a
purely functional approach in the 1960s and concurrent product and process
engineering in the 1970s, a cross functional emphasis was also used within
product development in the 1980s. Starting in the 1990s, customers and suppliers

33
Cf. Tan: A framework of supply chain management literature, p. 41, and Chandra and
Kumar: Supply chain management in theory and practice: A passing fad or a
fundamental change?, p. 100.

34
In fact, CIM became available in the 1980s. For an overview at the time see Milling,
Peter: Informationstechnologie als Wettbewerbsfaktor industrieller Unternehmen,
Beiträge des Fachbereichs Wirtschaftswissenschaften der Universität Osnabrück (No.
8614) 1986, Osnabrück, and a status report in the 1990s, see Milling, Peter: Die 'Fabrik
der Zukunft' in strategischer Perspektive, in: Milling, Peter and Günther Zäpfel (Eds.):
Betriebswirtschaftliche Grundlagen moderner Produktionsstrukturen, Berlin 1993.

35
Cf. Tan, Gek Woo, Michael J. Shaw and William Fulkerson: Web-based supply chain
management, in: Information Systems Frontier, Vol. 2 (2000), No. 1, January, p. 41,
and Chandra and Kumar: Supply chain management in theory and practice: A passing
fad or a fundamental change?, pp. 100-101.

36
See Weber, Jürgen, Andreas Bacher and Marcus Groll: Supply Chain Controlling, in:
Busch, Axel and Wilhelm Dangelmaier (Eds.): Integriertes Supply Chain Management:
Theorie und Praxis effektiver unternehmensübergreifender Geschäftsprozesse (2nd ed.),
Wiesbaden 2004, pp. 150-151.


Improving Supply Chain Performance

have been increasingly involved in the product development process. Deloitte
Research reports from 2000 onwards a further shift to a seamless integration
across the extended supply chain, allowing real-time development in a dynamic
partnering environment.37

Besides these more general developments, supply chain management and
logistics as related subjects developed quite differently in the Continental
European region and the Anglo-American area.38 The underlying basis of logistics
is the same in both regions. Originally, logistics encompassed the “physical
transfer activities, thus comprising all structures and processes which served the
purpose of transferring objects through space and time.”39 From there, its meaning
has been expanded in scope and has brought up an entire concept of logistics in
the Continental European area.

According to Göpfert, the development of the logistics discipline happened in
three phases. In the first phase, logistics was considered as a functional area with
an emphasis on goods transformation, including transportation problems. The
second phase extended this view with a more holistic coordination of material and
product flows within a system. Finally, the third and current phase refers to
logistics as a leadership concept and emphasizes the application of logistics as a
management paradigm.40

This development resulted in “a three level differentiation of the logistics term,
which can be regarded as generally accepted.”41 These levels are defined as
follows.

1st

- level: Logistics systems relate to the original logistics domain,
encompassing for example transportation, warehousing, and inventory
management (locally).

2nd

-  level: Logistics management is imposed on the first level with the
purpose of planning, controlling, and implementing logistics systems.
Mellios emphasizes the importance of logistics management to coordinate

37
Cf. Deloitte Research, n.a.: Creating unique customer experiences: The next stage of
integrated product development 2000, p. 8.

38
Cf. Delfmann, Werner and Sascha Albers: Supply chain management in the global
context, Working Paper Series of the Seminar für allgemeine Betriebswirtschaftslehre,
betriebswirtschaftliche Planung und Logistik (No. 102) 2000, Cologne, p. 6.

39
Delfmann and Albers: Supply chain management in the global context, p. 6.

40
Cf. Göpfert, Ingrid: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain
Managements, in: Busch, Axel and Wilhelm Dangelmaier (Eds.): Integriertes Supply
Chain Management: Theorie und Praxis effektiver unternehmensüber-greifender
Geschäftsprozesse (2nd ed.), Wiesbaden 2004, pp. 30-31.

41
Delfmann and Albers: Supply chain management in the global context, p. 6.


Supply Chain Management as a Strategic Option for Companies

different functions of the company.42 This view especially pointed out the
total cost view and the organizational impact of a cross-functional
approach, without using this terminology at the time. The logistics
management view builds the foundation for the subsequent ERP
movement.

3rd

- level: Logistics philosophy is the most far reaching level of the
Continental European logistics view and includes the following
principles: (1) systemic perspective and total cost approach, (2) flow-
orientation, i.e. process-oriented flow beyond functional and
organizational boundaries, and (3) customer- and service orientation, i.e.
companies of a supply chain are responsible and accountable for customer
satisfaction.43

In the Anglo-American region, the Council of Logistics Management as the
leading association in the field provided a widely accepted definition of logistics
in 1986:

“Logistics is the process of planning, implementing, and controlling the
efficient, cost-effective flow and storage of raw materials, in-process
inventory, finished goods, and related information from point-of-origin to
point-of-consumption for the purpose of conforming to customer
requirements.” 44

This definition clearly encompassed the first two levels of the Continental
European interpretation of logistics.45 However, in contrast to the Continental
European understanding, logistics was intended to stop at the organizational
boundaries.

Cooper, Lambert, and Pagh have concluded that “there is definitely a need for
the integration of business operations in the supply chain that goes beyond
logistics.”46 Consequently, they have stated: “The integration of business
processes across the supply chain is what we are calling supply chain

42
See Mellios, George G.: Logistics management: What, why, how, in: Journal of
Business Logistics, Vol. 5 (1984), No. 2, pp. 106-122.

43
Cf. Delfmann and Albers: Supply chain management in the global context, p. 7, and
Göpfert: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain
Managements , pp. 25-45 for a similar view.

44
Cf. Cooper, Martha C., Douglas M. Lambert and Janus D. Pagh: Supply chain
management: More than a new name for logistics, in: The International Journal of
Logistics Management, Vol. 8 (1997), No. 1, p. 1.

45 Cf. Delfmann and Albers: Supply chain management in the global context, p. 8.
46 Cooper, Lambert and Pagh: Supply chain management: More than a new name for
logistics, p. 1.


Improving Supply Chain Performance

management.”47 This notion reflects the evolutionary path logistics took after
Oliver and Webber’s introduction of the term SCM. Instead of further refining the
logistics discipline, SCM has evolved and disengaged from it.

Based on this discussion and development, the Council of Logistics
Management revised its definition of logistics in 1998 to better distinguish
logistics from supply chain management and explicitly included the supply chain
prcoess:48

“Logistics is that part of the supply chain process that plans, implements,
and controls the efficient, effective flow and storage of goods, services, and
related information from the point-of-origin to the point-of-consumption in
order to meet customers’ requirements.”49

Delfmann and Albers have concluded that the Anglo-American SCM concept
can be seen as a pragmatic representation of the theoretically more sophisticated
logistics philosophy.50 Though the logistics philosophy may be regarded as a
meta-concept and vision, it fails to specify its meaning well enough. This might be
the reason SCM has become the dominant term that relates to the holistic,
systemic, and inter-company organization view of management. In recognition of
this development, the Council of Supply Chain Management Professionals
(CSCMP), formerly known as the Council of Logistics Management,51 updated its
definition of the management of logistics:

“Logistics Management is that part of Supply Chain Management that plans,
implements, and controls the efficient, effective forward and reverse flow
and storage of goods, services, and related information between the point of
origin and the point of consumption in order to meet customers’
requirements.” 52

The key difference between these last two definitions can be seen in the fact
that the supply chain was previously perceived as a phenomenon. In the latter
definition from 2005, SCM has been acknowledged as a concept. In line with this

47 Cooper, Lambert and Pagh: Supply chain management: More than a new name for
logistics, p. 2.
48 Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 1-3.
49 Lambert, Cooper and Pagh: Supply chain management: Implementation issues and

research opportunities, p. 3.
50 See Delfmann and Albers: Supply chain management in the global context, p. 9.
51 The Council of Logistics Management changed its name to the Council of Supply

Chain Management Professionals (CSCMP). In 1985, the organization had already
changed its name from Council of Physical Distribution Management to Council of
Logistics Management, recognizing the growing field of logistics.

52
n.a.: Council of Supply Chain Management Professionals: http://www.cscmp.org, 2005,
retrieved on: February 15, 2006, see also its definition of SCM on page 20.


Supply Chain Management as a Strategic Option for Companies

perception, Lambert, Cooper, and Pagh attribute the early confusion between
logistics and SCM to the fact that the concept of logistics can be seen as a
functional silo within a company as well as a bigger concept that deals with the
management of material and information flows across the supply chain.53 They
have observed parallels to marketing, which can be seen as a functional discipline
as well as an overall company philosophy, for example the well-known customer
focus philosophy. They have concluded: “The understanding of SCM has been re-
conceptualized from integrating logistics across the supply chain to the current
understanding of integrating and managing key business processes across the
supply chain.”54 Figure B-1 illustrates this along the dimensions of theoretical
scope and practical applicability.

broad

-business processes
-systemic
optimization

Theoretical
Scope

narrow

-logistics function
-problem-oriented

Logistics
Philosophy

Supply Chain

(Cont.-Eur.)

Management
(Ang.-Am.)

Logistics Management
(Cont.-Eur.)
(Ang.-Am.)

Logistics Systems
(Cont.-Eur.)
Logistics & Operations
(Ang.-Am.)



high Practical low
-clear implementation Applicability -no direct implemenguidelines
tation guidelines
-defined procedures -meta guidelines /
philosophies

Figure B-1: Anglo-American vs. Continental-European understanding of logistics and SCM

It can be argued that the concept of SCM has developed in line with these
observations. This has stimulated many authors and researchers to come up with a

53
Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, p. 2.

54
Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 2-3.


Improving Supply Chain Performance

variety of interpretations and understandings of SCM and logistics and it has been
accompanied by countless definitions. Other authors have made great efforts to
synthesize all these different views in order to organize the field and to develop a
theory of SCM. The remainder of this section provides a meta-analysis of some
streams of thought and definitions and presents some synthesized views.55

Several definitions have been already introduced above. Definitions are an
important and necessary part of theory building for two reasons. First, definitions
communicate a picture of a phenomenon. Second, they avoid misunderstandings
that are caused by different personal or organizational perceptions of terms.56
Thus, definitions fulfill an important role. Providing definitions is not to be
mistaken with terminological discussions conducted in a way that they do not
contribute to the increase in the body of knowledge. Obviously, such discussions
should be avoided.57

In an attempt to summarize existing SCM literature at the time, Bechtel and
Jayaram have identified four generic schools of thought:58

1.
The functional awareness school. Authors representing this school recognize
the existence of a chain of functional areas. They agree that the supply chain
covers material flow from channel members or suppliers through end users.
Emphasis is placed on including all chain members from beginning to end.
2.
The linkage/logistics school. The linkage and logistics school reaches beyond
the functional awareness school and begins to address the material flows
through this chain. It identifies the linkages among functional areas that
generally includes suppliers, production, and distribution. Whereas the
functional awareness school merely recognizes the existence of linkages, the
linkage/logistics school investigates how linkages among the functional areas
can be exploited for competitive advantage, especially in the area of logistics
and transportation.
3.
The information school. Here, authors emphasize the bidirectional flow of
information between supply chain members and identify it as the backbone of
effective SCM. According to this school, companies that are prospering
55
The following meta-analysis is not intended to elaborate on the different SCM concepts

in detail. That will be provided in subsequent chapters.
56 See Schönsleben: Integrales Logistikmanagement: Planung und Steuerung der

umfassenden Supply Chain, p. 4. Schönsleben mentions a third reason, namely that

definitions simply belong to a scientific book.
57 For further reference, see for example Popper, Karl R.: The open society and its

enemies - Band 2: The high tide of prophecy: Hegel, Marx and the aftermath, 5th ed.,

London 1966, reprinted in 1973, p. 15.
58 Cf. Bechtel and Jayaram: Supply chain management: A strategic perspective,

pp. 16-18.


Supply Chain Management as a Strategic Option for Companies

appear to be those taking advantage of information technology on several
levels.

4.
The integration/process school. Under this school of thought, supply chain
areas are integrated into “a system defined as a set of processes that strive for
the best overall system, which adds value.”59 In contrast to the linkage school
that assumes functional areas appear in a fixed sequence that cannot be
changed, the integration/process school suggests that the emphasis is on
customer satisfaction and the configuration of the functional areas in the
supply chain can be changed if necessary.
Though there is not an intended hierarchy of the four generic schools of
thought, one can clearly observe that the schools differ in scope with the
integration school being the most far reaching. What becomes clear in their
analysis is that SCM includes issues of cooperative relations with supply chain
members.60

Harland has come to a similar conclusion but has identified four different uses
of the term SCM. In his analysis, the relational aspects of SCM have been
emphasized even more. Again, a clear hierarchy can be observed:61

1.
Internal view use. This view relates closely to preexisting concepts of
materials management and the value chain. SCM under this understanding is
limited to integrating business functions involved in the flow of materials and
information from inbound logistics to outbound distribution.
2.
Dyadic relationship use. Advocates of SCM in a dyadic relationship context
focus only on the management of two party relationships with immediate
suppliers.
3.
Chain of business use. Considering a chain of business, the perspective
extends beyond suppliers and customers to suppliers’ suppliers and
customers’ customers.
4.
Network use. Adopting a network view encompasses the management of an
entire network that is involved in the supply of a product or service to end
customers.
Göpfert has identified a dichotomy of definition groups. In definition group
one, Göpfert has summarized those definitions that show a direct connection to
business logistics. It emphasizes the logistics flow and the coordination of this
between companies and across supply chains. Definition group two, in contrast,
does not establish a direct link to logistics, instead emphasizing an inter-
organizational management of business processes. It relates more to cooperation

59 Bechtel and Jayaram: Supply chain management: A strategic perspective, p. 18.
60 See Bechtel and Jayaram: Supply chain management: A strategic perspective, p. 18.
61 Cf. Harland: Supply chain management: Relationships, chains and networks, p. S64.


Improving Supply Chain Performance

management or relationship management.62 Cooper, Lambert and Pagh have been
cited as a prominent example of this group: “The integration of key business
processes across the supply chain is what we are calling supply chain
management.”63

Another way to categorize existing SCM definitions has been provided by
Mentzer et al. Though their analysis has added substantially to the development of
a common understanding of the term SCM, the categories seem to be more a
mixture of levels of detail and emphasis than emphasis alone. In their view, the
most far reaching definitions see SCM as a management philosophy.64 This can be
compared to the integration/process school suggested by Bechtel and Jayaram and
the second definition group from Göpfert. In particular, SCM as a philosophy
“extends the concept of partnerships into a multiform effort to manage the total
flow of goods from the supplier to the ultimate customer.”65 The following
elements characterize the management philosophy view: (1) systems approach, i.e.
considering the supply chain as one entity, (2) strategic, cooperative orientation,
and (3) customer focus.

In another category, Mentzer et al. see SCM as a set of activities that
implement a management philosophy. In contrast to SCM as a management
philosophy, specific activities are identified to integrate a SCM philosophy. These
are: (1) integrating behavior, (2) sharing information mutually, (3) sharing risks
and rewards mutually, (4) cooperation, (5) having the same goal and the same
focus on serving customers, (6) integrating processes, and (7) building and
maintaining long-term relationships with partners.66

Those authors who focus on SCM as a set of management processes constitute
another group summarized by Mentzer et al.67 In contrast to the activities view,
processes are considered to be “a structured and measured set of activities
designed to produce specific output for a particular customer or market.”68 The
key difference from the activities view is that companies are to be organized
around processes with the objective of meeting the end customers needs best.

62 Cf. Göpfert: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain

Managements, pp. 28-32.
63 Cooper, Lambert and Pagh: Supply chain management: More than a new name for

logistics, p. 2.
64 Cf. Mentzer, John T. et al.: Defining supply chain management, in: Journal of Business

Logistics, Vol. 22 (2001), No. 2, p. 7.
65 Mentzer et al.: Defining supply chain management, p.7.
66 See Mentzer et al.: Defining supply chain management, pp. 7-10.
67 Cf. Mentzer et al.: Defining supply chain management, pp. 10-11.
68 Cf. Mentzer et al.: Defining supply chain management, p. 10; based on Davenport,

Thomas H.: Process innovation, Boston 1993, see also section B.III.2.a.


Supply Chain Management as a Strategic Option for Companies

Whereas some authors have a broad understanding of such a process view of
SCM, others have identified a set of specific processes that must be managed
across company boundaries. For example, Cooper, Lambert, and Pagh have
identified the following eight key SCM processes that have to be managed across
company boundaries: (1) customer relationship management, (2) customer service
management, (3) demand management, (4) order fulfillment, (5) manufacturing
flow management, (6) procurement, (7) product development and
commercialization, and (8) returns/reverse logistics.69 Chopra and Meindl have
identified three macro processes that all companies participating in a supply chain
have in common:70 (1) supplier relationship management, (2) internal supply chain
management,71 and (3) customer relationship management.

Discussions on SCM became more structured over the years. Table B-1
provides some recent definitions provided by selected, renowned authors. In light
of the discussions described above, they give a broad overview of the individual
views of the authors.

69
Cf. Cooper, Lambert and Pagh: Supply chain management: More than a new name for
logistics, p. 10.

70
Cf. Chopra, Sunil and Peter Meindl: Supply chain management: Strategy, planning, and
operation, 2nd ed., Upper Saddle River 2004, p. 17.

71
For the internal supply chain management process, Porter provides the well-known
framework of the value chain, see Porter, Michael E.: Competitive advantage, New
York 1985, pp. 33-61.


Improving Supply Chain Performance

Table B-1: Selected, recent SCM definitions, sorted by year

Author(s) Definition
Lambert et al. “SCM is the integration of business processes from end user
(1998), p. 1 through original suppliers that provides products, services, and
information that add value for customers and other
stakeholders.”
Mentzer et al. “SCM is the systemic, strategic coordination of the traditional
(2001), p. 18. business functions and the tactics across [these] business
functions within a particular company and across businesses
with the supply chain, for the purpose of improving the long-
term performance of the individual companies and the supply
chain as a whole.”
McCormack &
Johnson
(2001), p. 34
“SCM is the process of developing decisions and taking
actions to direct the activities of people within the supply chain
toward common objectives.”
Vakharia “SCM is the art and science of creating and accentuating
(2002), p. 496 synergistic relationships among the trading partners in supply
and distribution channels with the common shared objective of
delivering products and services to the ‘right customer’, in the
‘right quantity’, and at the ‘right time’.
Stadtler “SCM is the task of integrating organizational units along a
(2002), p. 9 supply chain and coordinating material, information, and
financial flows in order to fulfill (ultimate) customer demands
with the aim of improving competitiveness of a supply chain
as a whole.”
Kuhn & “SCM is the integrated, process-oriented planning and
Hellingrath management of material, information and financial flows along
(2002), p. 10 the entire value chain; from the customer to the supplier of raw
material […].”
Swaminathan “SCM is the efficient management of the end-to-end process,
& Tayur which starts with the design of the product or service and ends
(2003), pp. with the time when it has been sold, consumed, and finally,
1387-1388 discarded by the consumer. This complete process includes
product design, procurement, planning and forecasting,
production, distribution, fulfillment, after-sales support, and
end-of-life disposal.”
Simchi-Levi et “SCM is the process of planning, implementing and
al. (2003), p. 2 controlling the efficient, cost effective flow and storage of raw
materials, in-process inventory, finished goods, and related
information from point-of-origin to point-of-consumption for
the purpose of conforming to customer requirements.”


Supply Chain Management as a Strategic Option for Companies

Chen & “SCM, as we envision, is a novel management philosophy that
Paulraj (2004), recognizes that individual businesses no longer compete as
p. 147 solely autonomous units, but rather as supply chains.
Therefore, it is an integrated approach to the planning and
control of materials, services and information flows that adds
value for customers through collaborative relationships among
supply chain members.”
Göpfert “SCM is a modern concept of company networks to exploit
(2004), p. 32 inter-company success potentials by means of R&D, design
and steering of effective and efficient material, information
and financial flows.”
Busch & “SCM is the inter-company coordination of material and
Dangelmaier information flows among the entire value creation process –
(2004)72 from raw material over the individual processing steps to the
end consumer – with the goal to optimize the entire process in
terms of time and cost aspects.”
CSCMO “SCM encompasses the planning and management of all
(2005)73 activities involved in sourcing and procurement, conversion,
and all Logistics Management activities. Importantly, it also
includes coordination and collaboration with channel partners,
which can be suppliers, intermediaries, third-party service
providers, and customers. In essence, SCM integrates supply
and demand management within and across companies.”
Christopher
(2005), p. 5
“SCM is the management of upstream and downstream
relationships with suppliers and customers to deliver superior
customer value at less cost to the supply chain as a whole.”

The variety of definitions supports the view that, in spite of the achievements

so far and the commonalities the most important representatives of definitions

share, there still exists a need for developing a theory on SCM, which serves as a

72
Busch, Axel and Wilhelm Dangelmaier: Integriertes Supply Chain Management - einkoordinationsorientierter Überblick, in: Busch, Axel and Wilhelm Dangelmaier (Eds.):
Integriertes Supply Chain Management: Theorie und Praxis effektiver
unternehmensübergreifender Geschäftsprozesse (2nd ed.), Wiesbaden 2004, p. 5. Theyclaim that this definition – adopted from Scholz-Reiter and Jakobza, SCM – Überblick
und Konzeption, in HMD, No. 207 (1999), pp. 7-15 – represents the “bottom line”
definition of SCM in German-speaking countries, although they add that other authors
do alter it. This is well justified as firstly, it leaves many aspects out, which other
authors might consider as important, for example integration and quality aspects. And
secondly, it does not make sense to claim a “bottom line” definition for a subject like
SCM on a regional basis.

73
Cf. n.a.: Council of Supply Chain Management Professionals.


Improving Supply Chain Performance

framework for researchers and practitioners. This will be addressed in chapter

B.I.3. The next chapter discusses some controversial issues that have emerged
around the SCM evolution.
2.
Supply Chain Management as a Perspective for Inter-Company Management
Most views on SCM aim at the most comprehensive perception of the concept, i.e.
the consideration of an entire supply chain in the decision making process. With
such an understanding, Delfmann and Albers remarked that the proponents of this
view “run into the omnipotence trap, as then, SCM is just synonymous for
management.”74 Whereas this does not seem to be too much of a concern in the
Anglo-American SCM literature, Göpfert also favors definition group one, which
is closer to the original logistics domain.75 Therefore, the questions to be
addressed must be the following.

-
To what extent should SCM involve participants of a specific supply
chain?

-
What is meant by “a specific supply chain”?

-
Who actually “manages” the supply chain?

Some attempts to answer these questions in more detail are provided in
subsequent chapters, especially in chapter B.III.2. In short, the answers depend on
the specific circumstances of a given supply chain. However, developing a general
framework provides support for this process. Additionally, the underlying theory
of SCM must be developed so that the concept can be grounded on solid support
from empirical data.

Another discussion has emerged around the term SCM, reflecting the concerns
of many. It centers on the comparison of the terms supply chain, demand chain,
distribution chain, supply network, supply web, and value system.

Several authors have argued in favor of the term “demand chain management”
in order to emphasize that all upstream activities in a supply chain are demand-
driven, triggered by the ultimate demand, and therefore “causing” the chain to
exist.76 However, emphasizing the importance and role of the ultimate demand for
a supply chain, all upstream activities represent supply relative to the ultimate

74 Cf. Delfmann and Albers: Supply chain management in the global context, p. 5.
75 See Göpfert: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain

Managements, p. 32 and section B.I.1., p. 16 in this text.
76 See, for example, Christopher: Logistics and supply chain management: Creating value-

adding networks, p. 5, or Frohlich, Markham T. and Roy Westbrook: Demand chain

management in manufacturing and services: Web-based integration, drivers and

performance, in: Journal of Operations Management, Vol. 20 (2002).


Supply Chain Management as a Strategic Option for Companies

demand. Therefore, both views accept the importance of demand and there is no
solid ground for a terminological change because of this. SCM does not rule out
customer focus per se and a distinction or even replacement of the term supply is
rejected.77 As Horvath remarked: “[…] the distinction between supply and demand
chains seems increasingly arbitrary.”78

Other authors have noted that the terms “network”, “web”, or “system” are
more accurate descriptions for what is known as a “chain” since companies
normally transform inputs from many suppliers into one or more outputs often for
many different customers.79 However, especially when adopting a process view, as
described in detail in chapter B.III.2., it can be argued that it indeed does fit the
description of a chain. Although there might be many suppliers of raw materials
and components throughout the supply process, in the end it is all merged into one
product or service. This entire process can therefore be seen as a chain from a
consumer’s perspective. Additionally, the physical flow of a supply chain routes
through the system in only one sequential direction. Accompanying information
and financial flows, however, can go both ways supporting a network view. Based
on this, the term supply chain network can be used as a synonym to supply chain
when writing about supply chains.80 The term “supply chain” does recognize the
most common supply chain structures without ruling out inherent branch
structures in supply chains. For that reason and for the sake of continuity, there is
no fundamental need to replace the term supply chain and SCM should not be
renamed on the basis of this argumentation.81

77
Cf. Vakharia, Asoo J.: E-business and supply chain management, in: Decision Sciences,

Vol. 33 (2002), No. 4, Fall, pp. 495-496.
78 Horvath, Laura: Collaboration: The key to value creation in supply chain management,

in: Supply Chain Management: An International Journal, Vol. 6 (2001), No. 5, p. 206.
79 Most authors who challenge the traditional terminology propose these variations, such

as Chopra and Meindl: Supply chain management: Strategy, planning, and operation, p.

5; Christopher: Logistics and supply chain management: Creating value-adding

networks, p. 5; McCormack and Johnson: Supply chain networks and business process

orientation: Advanced strategies and best practices; or Delfmann and Albers: Supply

chain management in the global context, p. 5.
80 Cf. Hertz, Susanne: Dynamics of alliances in highly integrated supply chain networks,

in: International Journal of Logistics: Research and Applications, Vol. 4 (2001), No. 2,

p. 239; and McCormack and Johnson: Supply chain networks and business process
orientation: Advanced strategies and best practices, pp. 2-4.
81
This is in line with the opinion of several authors, such as Werner, Hartmut: Supply
Chain Management: Grundlagen, Strategien, Instrumente und Controlling, 2nd ed.,
Wiesbaden 2002, p. 14; Delfmann and Albers: Supply chain management in the global
context, p. 5; or Knolmayer, Gerhard, Peter Mertens and Alexander Zeier: Supply chain
management based on SAP systems, Berlin Heidelberg New York 2002, p. 3.


Improving Supply Chain Performance

Porter has introduced the terms “value chain” and “value system” when
referring to internal operations and industries.82 This puts emphasis on the value
creation process of supply chains. Ultimately, all activities and processes aim to
bring value to the consumer, be it as a product or a service. The entire process is
therefore a value creation process. In the end, value is expressed by the
willingness of consumers to pay a certain price for the product or service. From a
business process perspective, activities that do not add value are waste and should
be eliminated. Waste has been defined as “[…] anything other than the minimum
amount of equipment, materials, parts, space, and time which are absolutely
essential to add value to the product.”83 This is especially important in highly
competitive markets. Though the term “value” better describes the processes
within the value creation process, there are also arguments that support the
traditional and established terminology. In accordance with the explanations given
above, from a consumer perspective all activities are supply activities and
therefore all preceding activities and processes of the total value created can also
be seen as supply that contributes to this value.

Weighing the arguments described, it can be concluded that the term SCM
should be used in the future as the sole reference for contributions in the field. It
should be accepted that it covers also all aspects and variations described above.
Introducing different terms without a solid, scientific foundation causes confusion
and does not provide any contribution. Nonetheless, the terms supply chain and
supply chain network may be used alternatively as they represent a description and
do not describe an entire field of research.

3. Concepts of Supply Chain Management
3.a. Supply Chain Management Objectives and Strategic Relevance
As definitions and understandings of SCM vary, so do objectives related to SCM.
In order to structure the stated goals, a three level hierarchy can be identified: (1)
strategic objectives, (2) tactical objectives, and (3) operational objectives.84

Many goals in literature are phrased in a way that disregards strategic tradeoffs.
For example, Knolmayer, Mertens, and Zeier have noted the strategic SCM
objective of maximizing customer and business value at the lowest total cost.85
Porter remarked, however, that strategy always involves trade-offs and decisions

82 See Porter: Competitive advantage, pp. 34-35.
83 Suzaki, Kiyoshi: The new manufacturing challenge, New York 1987, p. 250.
84 See Stevens, Graham C.: Integrating the supply chain, in: International Journal of


Physical Distribution & Materials Management, Vol. 19 (1989), No. 8, p. 4. This

classification is an appropriate framework to classify SCM objectives.
85 Cf. Knolmayer, Mertens and Zeier: Supply Chain Management Based on SAP Systems,

p. 7.

Supply Chain Management as a Strategic Option for Companies

on what to do and what not to do.86 Therefore, statements like the one mentioned
above have to be placed in perspective. In the field of Operations Management,
the theory of performance frontiers provides explanation for this.87 In variations
also known as ‘production frontier’ or ‘trade-off curve’, it is mainly based on
economic theory. According to this, a production frontier describes the maximum
output that can be produced from any input combination, given existing
technology.88 Thus, before elaborating on the previously mentioned three
objective categories, a note on performance frontiers seems to be appropriate.

Schmenner and Swink have expanded the scope of this definition in two ways.
Firstly, instead of merely encompassing a production output, performance
encompasses multidimensional elements, including all kinds of possible
performance elements, such as quality, product variety, or flexibility.89 Secondly,
the performance frontier itself encompasses two elements. (1) The operating
frontier consists of a set of operating practices and policies on an aggregate level,
such as quality management, process reengineering, just-in-time (JIT) and so on.
Theoretically, applying all available management practices and tools to the fullest
extent limits the performance at any given cost level, subject to the asset frontier,
which is described next. (2) The asset frontier limits the achievable performance at
any given cost level due to physical or technological restrictions. Either one, the
operating frontier or the asset frontier, can restrict overall performance at a given
cost level. The restrictive frontier will be the one whose graph lies below the
other, see Figure B-2.90 The absolute performance limit, then, defines maximum
achievable performance independently from cost. This restricts therefore
achievable performance. Up to the absolute performance limit, the marginal
performance gains tend to decrease. Besides the simple monotonic shape depicted
in Figure B-2, the efficient performance frontier could also take an S-shape
course.

86 See Porter, Michael E.: What is strategy? in: Harvard Business Review, Vol. 74 (1996),

No. 6, pp. 68-70.87 Cf. Schmenner, Roger W. and Morgan L. Swink: On theory in operations management,

in: Journal of Operations Management, Vol. 17 (1998), pp. 107-110.
88 See Samuelson, Paul: Foundations of economic analysis, Cambridge, MA 1947, chapter

4, taken from Schmenner and Swink: On theory in operations management, p. 107. On

production frontiers, any introductory business textbook provides assistance, especially

see Gutenberg, Erich: Grundlagen der Betriebswirtschaftslehre. Band I: Die Produktion,

24th ed., Berlin Heidelberg New York 1983, pp. 303-327.
89 Schmenner and Swink include costs as a possible performance element. As this is the

counterpart on the vertical axis, this seems inappropriate.
90 Cf. Schmenner and Swink: On theory in operations management, pp. 107-109.


Improving Supply Chain Performance


Performance

rabsolute performance limit
operatingfrontier
efficient= efficient performance
e
i
t
n
ro
f
et
asst
n
ie
frontier

c
effi
Cost

Figure B-2: Efficient asset and operating frontiers91

A distinction between asset frontiers and operating frontiers has been also
elucidated. The asset frontier limits performance mainly due to technological
restrictions. In contrast, the operating frontier limits performance mainly because
of managerial and organizational limitations. Usually, one of the two limits overall
performance, though they can be considered to be considerably interdependent.
For example, in Figure B-2, the operating frontier is considered to be the limiting
factor. The introduction of new management practices and concepts, such as SCM,
can lead to a betterment of the efficient performance frontier, i.e. a shift to the
left/upper left. SCM as a set of management practices affects mainly the operating
frontier. If this betterment exceeds the asset frontier, the asset frontier becomes the
new limiting factor. Technological improvements, such as with e-business
capabilities, tend to affect the asset frontier.

What is termed betterment in this context is in contrast to improvement.
Whereas the term improvement refers to the improvement of performance within a

91
In contrast to the original illustration, performance frontiers are differentiated. The
absolute limit of performance mainly due to technological limitations, no matter the
cost, limits possible absolute performance. The efficient performance frontier depicts
the most efficient realization of any performance level, utilizing best practice
management practices and most efficient technology. This is reflected by efficient asset
and operating frontiers.


Supply Chain Management as a Strategic Option for Companies

given efficient performance frontier, betterment refers to a shift of the efficient
performance frontier all together, as illustrated in Figure B-3.92

Performanceefficient
performancefrontier
absolute performance limit
A
improvement
betterment
betterment
A''
A'
Cost

Figure B-3: Effect of improvement and betterment on the efficient performance frontier

A company in position A can improve its performance to A’ and A’’, doing the
same at less cost or doing better at the same cost. A company also can witness a
shift in the efficient performance frontier without embracing its benefits. Hence,
betterment only addresses the potential for improvement by shifting the
performance frontier. If a company fails to adopt operating practices or
technological advances made available by an advancing frontier, no improvement
takes place for that company. In fact, the advanced frontier places that company at
a greater competitive disadvantage than before the advance occurred.

As an extension of this view, there is an additional aspect to consider. Due to
differentiation strategies and individual customer perceptions, it is rather
impossible to define efficient performance frontiers as if they relate to many
products or even product groups. A superior performance to one may only be
marginal to someone else even in the case of homogeneous products. Thus, a
differentiated view should be reflected as well. Figure B-4 accounts for this by
adding a third dimension: differentiation. Adding this categorical dimension
allows rotation around the performance dimension and therefore defines different

92 See Schmenner and Swink: On theory in operations management, pp. 109-110.


Improving Supply Chain Performance

efficient performance frontiers along the way. Figure B-4 shows one such
“performance frontier slice” for a specific, differentiated product or product group.
For each dotted line in the differentiation circle, such a “performance frontier
slice” exists. Thus, efficient performance frontiers are highly situational and
product-specific. The differentiated products, however, are still interdependent,
depending on demand elasticity or more precisely product-specific demand
elasticity. According to the underlying economic theory, however, a trade-off is
inevitable and this is of high relevance in the context of objective definitions in
SCM.

Differentiation
Performance
"slices" of
differentiation
Circle
efficient
performancefrontier
Cost
absolute performance limit
Figure B-4: A third dimension for performance frontiers

It should be noted here that the SCM objective of improving cost and
performance together would imply the betterment of the efficient performance
frontier. Thus, this should better be phrased in a way to state that SCM promises
higher customer and business value (performance) at a given cost level or a lower
cost level at a given customer and business value proposition. Based on this
analysis and the objectives identified in literature, it can be concluded that the
availability of the SCM concept promises not only a betterment of performance
frontiers, but also that partial adoption can lead to improvements on the individual
company level below the efficient performance frontier to approach the efficient
performance frontier.

With this in mind, the following objectives are identified. At the strategic
level, objectives are related to the strategic orientation of a supply chain.93 This

93 See Stevens: Integrating the supply chain, p. 4.


Supply Chain Management as a Strategic Option for Companies

includes first and foremost the definition of competitive priorities with regard to
supply chain capabilities. The major dimensions of competitive priorities include
cost, quality, time, and flexibility.94 Only a few proponents can be found that
explicitly point out strategic objectives for SCM. Often, authors only mention
optimization potentials and by that implicitly reduce SCM effects to
rationalization effects.95 One rather simplistic view has been expressed by New,
who compressed the benefits claimed by SCM – namely lower costs, improved
quality, more effective technological development, and reduced lead times – into
the one dimension “efficiency”, i.e. “doing better with the same or less investment
or resources.”96 A similar approach has been adopted by Delfmann and Albers,
who note that a frequently mentioned objective of SCM is the reduction of cost.97
They have also acknowledged, however, that building a competitive advantage is
an essential goal of SCM. The following strategic objectives and goals can be
identified in literature as strategic objectives by means of SCM:

-
Maximal customer and business value at the lowest possible total cost.98

-
Superior speed-to-market by means of agility at the lowest possible
costs.99

-
Fulfillment of a desired level of customer service performance.100

94
Cf. Krajewski, Lee J. and Larry P. Ritzman: Operations management, 7th ed., Upper
Saddle River 2005, pp. 62-67. Other textbooks vary these elements, for example Slack,
Nigel, Stuart Chambers and Robert Johnston: Operations management, 4th ed., Harlow
2004, pp. 44-57 divide the time dimension up into speed and dependability or Russell,
Roberta S. and Bernard W. Taylor III: Operations management, 3rd ed., Upper Saddle
River 2000, pp. 32-35 replace time by speed. However, the essence remains the same.
Traditionally, three dimensions have been used as strategic success factors, see Kaluza,
Bernd and Guido Klenter: Zeit als strategischer Erfolgsfaktor von
Industrieunternehmen, Diskussionsbeiträge des Fachbereichs
Wirtschaftswissenschaften der Universität - Gesamthochschule - Duisburg (No. 173)
1992, Duisburg, pp. 14-18; and Sommerlatte, Tom and Michael Mollenhauer: Qualität,
Kosten, Zeit - Das magische Dreieck, in: Little, Arthur D. (Ed.): Management von
Spitzenqualität,Wiesbaden 1992, pp. 26-36.

95 Cf. Göpfert: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain
Managements, p. 33.
96 Cf. New: The scope of supply chain management research, p. 20. Here, New mixes

performance measures with cost parameters, which increases complexity.
97 Cf. Delfmann and Albers: Supply chain management in the global context, p. 10.
98 See Knolmayer, Mertens and Zeier: Supply Chain Management Based on SAP

Systems, p. 7.

99
Cf. Samaranayake, Premaratne: A conceptual framework for supply chain management:
a structural integration, in: Supply Chain Management: An International Journal, Vol.
10 (2005), No. 1, p. 48.


Improving Supply Chain Performance

-
Improvement of competitiveness.101

Strategic objectives all relate to the generally accepted aim of a company to
achieve a competitive advantage and therefore superior profitability.102 Alternative
objective statements on the strategic level are superior customer service level or
business excellence.103

Most authors have focused on tactical and operational objectives. Tactical
objectives can be considered to link operating objectives to strategic objectives. In
this role, they represent a more abstract aggregation of operating objectives. Thus,
they can hardly be measured directly. Operational objectives, in contrast, are
directly measurable and have mostly an obvious, immediate effect. Usually, they
are then linked directly or through mediating performance measures to strategic
objectives. As with other management concepts, like Total Quality Management
(TQM), authors hardly leave out any performance measure that could be
positively affected by the SCM concept. In order to provide an overview of the
most commonly identified objectives, Table B-2 summarizes tactical SCM
objectives and Table B-3 shows operational SCM objectives. Arrows in the table
indicate the direction of change in order to achieve improvements.

In order to organize goal items mentioned in literature, the individual goal
items can be summarized as follows. On the tactical level, four goal categories can
be identified:

-
Communication, i.e. better communication.

-
Collaboration, i.e. improved synchronization and management control,
joint optimization, better external integration, a reduction of complexity
and strengthening relationships. Also, lower transaction costs can be
considered as a collaboration related objective.

-
Customer orientation, i.e. higher customer satisfaction and service and
more customer orientation.

100
This objective definition is rather unspecific, see Stevens: Integrating the supply chain,

p. 3.
101
Cf. Stadtler, Hartmut: Supply chain management - an overview, in: Stadtler, Hartmut
and Christoph Kilger (Eds.): Supply chain management and advanced planning:
concepts, models, software and case studies (2nd ed.), Berlin Heidelberg New York
2002, p. 8.

102
For example, see Mentzer et al.: Defining supply chain management, pp. 12-19; and
Porter: Competitive advantage, p. 3.

103
See Kanji, Gopal K. and Alfred Wong: Business excellence model for supply chain
management, in: Total Quality Management, Vol. 10 (1999), No. 8, pp. 1150-1152; and
Delfmann and Albers: Supply chain management in the global context, p. 10.


Supply Chain Management as a Strategic Option for Companies

-
Flexibility, i.e. more resource flexibility, better availability, better
adaptability and faster time-to-market.

Table B-2: Identified tactical SCM objective items

Tactical
Objectives Knolmayer,
Mertens and Zeier
(
2002)
,
p.
7
Nicolai (
2001)
,
p.
2
Busch and Dangelmaier (
2004)
,
pp.
8-
9 Göpfert (
2004)
,
pp.
33-
35
Schönsleben (
2004),
pp.
35-
37
Mentzer et al.
(
2001)
,
p.
15
Stank,
Keller and Daughertyt
(
2001)
,
p.
29
Croom (
2005)
,
p.
60 Deloitte study (1999)
104Simchi-
Levi,
Kaminsky and
Simchi-
Levi
(
2003),
p.
255
. customer satisfaction
/ service X X X X X
. information sharing X X X X
. synchronization/
management control X X X X
. (resource) flexibility X X X
. availability X X X
. joint optimization X X
. external integration X X
. complexity X X
. customer orientation X
. adaptability X
. time-to-market X
. strength of
relationships X
. indicates higher values for improvement and . indicates a lower value for improvement

On the operational level, three goal categories can be identified:

-
Fulfillment, i.e. faster delivery times, better on-time delivery, shorter lead
times and faster order fulfillment times.

104 Cf. Deloitte Consulting, n.a.: Energizing the supply chain: Trends and issues in supply
chain management 1999, p. 21.


Improving Supply Chain Performance

-
Efficiency, i.e. lower cost per unit (inventory, procurement, production,
distribution and administration), higher utilization, reduction of waste and
non-added value activities, seamless processes and reduction of redundant
work.

-
Quality, i.e. improved product quality, higher process quality.

Since many authors have referred to SCM as a concept assumed of improving
operational performance, improvements in competitiveness are to diffuse through
this better operational performance, giving SCM strategic relevance. Despite this
lack of precision, SCM is still considered to be a strategic management concept;
though, according to Schönsleben, it is one that focuses as no other on delivery
and processes.105 Furthermore, decisions made in SCM are also of a strategic
nature that is to say they have long-term implications in order to achieve
objectives.

The strategic relevance of SCM can be justified by means of the resource-
based view of competitive advantage. Initially, the resource-based view of the
firm has focused on internal resources rather than products as sources of
competitive advantage.106 Prahalad and Hamel have referred to such resources as
core competencies of a company that, in order to attain strategic relevance, should
be applicable to a wide variety of markets, be of significant value to the end
customer, and be hard to imitate. They point out that such competencies are most
likely to occur as “[…] a complex harmonization of individual technologies and
production skills.”107

Barney has distinguished between competitive advantage and sustained
competitive advantage. Whereas competitive advantage is a “[…] value creating
strategy not simultaneously being implemented by any current or potential
competitor”, a sustained competitive advantage implies that current and potential
competitors are unable to duplicate that strategy.108 According to Barney, firm
resources must possess four attributes in order to be a source of sustained
competitive advantage: (1) they have to be valuable for a company and its
environment, (2) they have to be rare, (3) they have to be imperfectly imitable,

105
Cf. Schönsleben: Integrales Logistikmanagement: Planung und Steuerung der um


fassenden Supply Chain, p. 35.
106 Cf. Wernerfelt: A resource-based view of the firm, pp. 171-180.
107 Prahalad, C.K. and Gary Hamel: The core competence of the corporation, in: Harvard

Business Review, Vol. 68 (1990), May/June, pp. 83-84.

108
Cf. Barney: Firm resources and sustained competitive advantage, pp. 102-103. In
addition, Barney points out that this does not mean that a sustained competitive
advantage necessarily lasts forever. Changes in the economic structure of industries
may nullify the advantage, which in turn indicates that these unique capabilities are not
of value anymore in the new structure.


Supply Chain Management as a Strategic Option for Companies

and (4) no substitutes for an otherwise valuable, rare, and imperfectly imitable
resource are available.109

Table B-3: Identified operational SCM objectives

Operational
Objectives Wannenwetsch
(2005), p.
4Knolmayer,
Mertens
and Zeier
(2002), p.
7
Nicolai
(2001),
p.
2
Busch and
Dangelmaier (
2004),
pp.
8-9
Göpfert
(2004), pp. 33-35Schönsleben (
2004),
pp.
35-
37Zadek (2001), p.
329
Mentzer et
al.
(
2001),
p.
15Stank, Keller
and
Daughertyt(2001),
p.
29
Croom
(2005), p.
60Frohlich
and
Westbrook (2001)
,
p.
194
Deloitte
study
(1999)
110Simchi-Levi,
Kaminsky and
Simchi-Levi (2003),
p.
255
.
inventories X X X X X X X
.
procurement costs X X X X X X X
.
production costs X X X X X X
.
distribution costs X X X X X X
.
on-time delivery X X X X X X
.
lead times X X X X X
. process quality/
seamless processes X X X X X
. order fulfillment
time X X X X
.
utilization X X X X
. waste111/ non-value
added activities X X X
. administration costs/
redundancies X X X X
.
product quality X X X
.
delivery times X X X
. indicates higher values for improvement and . indicates a lower value for improvement

109 Cf. Barney: Firm resources and sustained competitive advantage, pp. 105-112.
110 Cf. n.a.: Energizing the supply chain: Trends and issues in supply chain management, p.

21.
111 If not explicitly stated as an operational objective item in itself, this refers to the seven
classic wastes, introduced by Shigeo Shingo: overproduction, waiting, transportation,
unnecessary processing steps, stocks, motion, and defects, see Slack, Chambers and
Johnston: Operations management, pp. 524-525.


Improving Supply Chain Performance

The resource-based view builds the foundation for the relational view. The
relational view has expanded the unit of analysis to dyadic and network firm
relationships and thereby implicitly to supply chain networks. The sources of
sustained competitive advantage are no longer seen within a firm’s boundaries but
in resources that attain relevance only by combining two or more independent
firms. Dyer and Singh have defined benefits based on and grounded in
relationships as relational rents. They have stated that “[…] relational rents are
possible when alliance partners combine, exchange, or invest in idiosyncratic
assets, knowledge, and resources/capabilities, and/or they employ effective
governance mechanisms that lower transaction costs or permit the realization of
rents through the synergistic combination of assets, knowledge, or capabilities.”112
Relational rents are preserved because it is almost impossible to replicate such a
distinctive, socially complex institutional environment with its formal and
informal rules controlling opportunism and/or encouraging cooperative behavior.
More precisely, such an advantageous constellation implies that the source of
advantage cannot be traced by competitors because

-
competitors are unable to replicate the resources due to causal ambiguity,

-
time delays of beneficial successive relational investments cause
prohibitively high costs of replication,

-
no partners possessing the necessary complementary resources or
relational capability are available, and

-
capabilities have grown indivisible due to coevolution with the partnering
firm.113

Consequently, relational rents based on resources developed within supply
chains are harder to imitate than company-specific resources because of their
higher complexity. They are also, however, more difficult to establish and require
more resources and strategic consideration. In conclusion, relational rents
materialize in a variety of operational and tactical objectives as identified in this
section. Strategic relevance is achieved through the relational view, which
essentially is an expansion of the resource-based view of the firm, and its
implications for sustained competitive advantage.

The level of objectives – strategic, tactical, and operational – is independent
from the view of SCM and authors representing the holistic, systemic perspective
as well as the ones proposing a more narrow, limited perspective, argue for the

112 Dyer, Jeffrey H. and Harbir Singh: The relational view: Cooperative strategy and

sources of interorganizational competitive advantage, in: Academy of Management

Review, Vol. 23 (1998), No. 4, p. 662.
113 Cf. Dyer and Singh: The relational view: Cooperative strategy and sources of

interorganizational competitive advantage, pp. 671-674.


Supply Chain Management as a Strategic Option for Companies

above stated goals. The relational view, however, emphasizes that strategic
importance increases with a more holistic approach because constellations are
more likely to carry a sustained competitive advantage as complexity increases.
Many diverse concepts and models of SCM have been suggested with an aim to
apply the concept in a way that improves performance and achieves the previously
discussed objectives and sustained competitive advantage. To give an overview of
the different perspectives on SCM, these approaches are introduced in the
following section. Following this discussion, a comprehensive third generation
model of SCM is introduced.

3.b. Elements of Supply Chain Management and Underlying Theories
Stevens, as one of the early proponents of the total integration approach, has
identified four stages of progression towards an integrated supply chain. In the
first stage, called “baseline”, companies operate with separate departments that
carry their own responsibility. There are no integrative efforts visible even within
functions. The second stage, “functional integration”, integrates the activities
within the functions. This aggregation is reflected in common organizational
structures where business units are organized through functional departments.
Stage three leads to “internal integration”. Here, functional boundaries are
overcome in order to manage material and information flows smoothly within a
company or a business unit. Stevens remarked that internal integration is still
reactive to customer demand rather than managing of demand together with
customers. Therefore, Stevens has introduced stage four, “external integration”.
Key changes within the organization that accompany external integration include a
customer-oriented focus and a change in attitude towards non-adversarial
relationships with suppliers along the supply chain, characterized by mutual
support and cooperation.114

The following concepts have picked up these key characteristics and have
expanded the idea further. Bechtel and Jayaram not only brought together
definitions of SCM, leading to the four schools of thought described earlier, but
also have analyzed elements of SCM. Based on this analysis, they have submitted
two areas of SCM elements: content elements and process elements. As content
elements, they have identified the importance of designing the product and the
information flow around the customer. This spans from the design stage, over
procurement, storage, manufacturing, warehousing, distribution to recycling.
Thus, on the content side, they have emphasized the holistic approach.115

As process elements, five process areas have been identified that are relevant
for SCM: (1) planning, (2) implementation, (3) inter-organizational structure, (4)
measurement, and (5) IT. The planning process consists of TQM practices,

114 Cf. Stevens: Integrating the supply chain, pp. 6-8.
115 Cf. Bechtel and Jayaram: Supply chain management: A strategic perspective, p. 20.



Improving Supply Chain Performance

systems thinking, cost analysis modeling, and (process) reengineering.
Implementation is concerned with the implementation sequence of SCM. Inter-
organizational structures are mainly concerned with cooperative relationships and
partnerships. The measurement process points out that measurement systems are
mainly directed to individual companies instead of entire supply chains. The IT
process deals with issues if data and information exchange, data storage, and data
usage. In conclusion, Bechtel and Jayaram have identified areas of interest for
SCM and have illustrated the importance of a systemic and expanded view. This
includes also the key elements identified by Stevens, namely customer focus and
cooperative relationships. As a third, major block for SCM, Bechtel and Jayaram
have put IT in the overall context of SCM.116

The scope of a supply chain to be managed involves first and foremost the
number of firms involved and their functions and activities.117 In their literature
review, Cooper, Lambert, and Pagh have come to the conclusion that the functions
and activities mostly agreed on are the integration of information systems, joint
planning and control activities, and cooperative efforts in the process areas.118 The
integrated supply chain is characterized by a change towards customer orientation
and cooperative relationships, just as envisioned by Stevens.119 Their framework
for SCM identifies the following eight processes that are connected by
information flows and have to be managed across supply chain partners:

(1) customer relationship management, (2) customer service management,
(3) demand management, (4) order fulfillment, (5) manufacturing flow
management, (6) procurement, (7) product development and commercialization,
and (8) returns/reverse logistics. These processes are then to be managed by
means of the management components depicted in Figure B-5.
116
Cf. Bechtel and Jayaram: Supply chain management: A strategic perspective,
pp. 20-25.

117
Cf. Cooper, Lambert and Pagh: Supply chain management: More than a new name for
logistics, p. 8.

118
Cf. Cooper, Lambert and Pagh: Supply chain management: More than a new name for
logistics, pp. 8-9.

119
Cf. Stevens: Integrating the supply chain, p. 8.


Supply Chain Management as a Strategic Option for Companies

Physical & Technical
Management Components
Planning and
Control Methods
Work Flow/
Activity Structure
Organization
Structure
Communication and
Information Flow
Facility Structure
Product Flow
Facility Structure
Managerial & Behavioral
Management Components
Management
Methods
Power and Leadership
Structure
Risk and Reward
Structure
Culture and
Attitude
Figure B-5: Fundamental operational SCM components120

The supply chain business processes noted by Cooper, Lambert, and Pagh
match well with the elements identified by Bechtel and Jayaram. Nevertheless,
Cooper, Lambert, and Pagh’s SCM components are more detailed and better
illustrate the influence the SCM concept has on nearly all management elements
than the related process elements provided by Bechtel and Jayaram do. Both,
however, have especially emphasized the importance of the information flow in
their models.121

Business processes are also central to the view Chopra and Meindl have taken
on SCM. They have summarized three SCM macro processes that have to be well
integrated and mirrored along the supply chain. These are: (1) customer
relationship management, (2) internal supply chain management, and (3) supplier
relationship management. As key characteristics for successful management of
these macro processes, they identify communication and coordination between the

122

process owners.

120 Lambert, Cooper and Pagh: Supply chain management: Implementation issues and

research opportunities, p. 12.
121 See Cooper, Lambert and Pagh: Supply chain management: More than a new name for

logistics, pp. 5-6; and Bechtel and Jayaram: Supply chain management: A strategic

perspective, pp. 19-25.
122 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 17.

Improving Supply Chain Performance

Cross has placed special emphasis on the role of IT in the context of future
supply chains. Though less well grounded in literature, the outlined characteristics
fit well into the stream of the developing SCM. Cross has envisioned future supply
chains as transparent, timely, and tuned. Transparency refers to extensive
information sharing on capabilities, capacities, inventories, and plans between
systems. In this view, humans should be freed as much as possible from repetitive
tasks. With timeliness, Cross has also emphasized customer orientation. Tuning
aims at collaborative relationships, which includes the sharing of knowledge in
order to better meet customer requirements.123

Kanji and Wong have derived a business excellence model that hypothesizes
that leadership positively influences customer focus, cooperative relationships,
management by fact, and continuous improvement.124 These four factors are
supposed to lead to SCM excellence. They have tested this model with structural
equation modeling. As a result, leadership was shown to have a positive influence
on all four factors. More importantly, only customer focus and cooperative
relationships had a significant relationship with SCM excellence. These two
factors can also be found in other models and this underlines their importance.

A similar view is supported by Stank, Keller, and Daugherty. They have
emphasized the three elements integration, coordination, and collaboration across
organizations, incorporating a customer focus.125 This spans all major business
processes.126 They have not differentiated well, however, between the three
elements and have combined the elements into the factors internal collaboration
and external collaboration. According to this analysis, they have tested the effect
of these two factors on logistical service performance. External collaboration
seemed to be a necessary, but not sufficient condition for increased logistical
service performance. Internal collaboration was found to mediate the relationship
between external collaboration and logistical service performance.127 Bask and
Juga’s view of SCM can be seen in a similar way. They have named only

123 Cf. Cross, Gary J.: How e-business is transforming supply chain management, in:

Journal of Business Strategy (2000), March/April, p. 39.
124 Cf. Kanji and Wong: Business excellence model for supply chain management.
125 Cf. Stank, Theodore P., Scott B. Keller and Patricia J. Daugherty: Supply chain

collaboration and logistical service performance, in: Journal of Business Logistics, Vol.

22 (2001), No. 1, p. 30.
126 In essence, this refers to processes as outlined by Cooper, Lambert and Pagh: Supply

chain management: More than a new name for logistics, p. 10; Bechtel and Jayaram:

Supply chain management: A strategic perspective, p. 20; or Chopra and Meindl:

Supply chain management: Strategy, planning, and operation, p. 17.
127 Cf. Stank, Keller and Daugherty: Supply chain collaboration and logistical service

performance, p. 40.


Supply Chain Management as a Strategic Option for Companies

integration and collaboration as the “dominant logic in SCM” while combining
coordination with collaboration.128

Based on an extensive literature review and a joint research effort, Mentzer et
al. have developed a supply chain model that aims to merge existing
understanding into one joint model.129 Their model has been suggested as the basis
for further research in the field. Mentzer et al. have differentiated between a SCM
philosophy and SCM itself. The supply chain philosophy is referred to as supply
chain orientation and comprises a systemic view of supply chains as a whole, a
strategic orientation towards collaborative relationships, and a customer focus.130
Supply chain orientation is seen as a prerequisite to SCM. It is important to note
that it is not sufficient if only one company in a supply chain adopts a supply
chain orientation and not the other supply chain partners. SCM then transfers the
philosophy into practice. Willingness to address the following factors has been
identified as an antecedent to supply chain orientation:131 (1) trust, (2)
commitment, (3) interdependence, (4) organizational compatibility, (5) vision, (6)
key processes, (7) necessity of a leading firm, and (8) top management support.
Based on supply chain orientation, SCM then is characterized by inter-company
management elements. Mentzer et al. have identified the following: (1)
information sharing, (2) shared risks and rewards, (3) cooperation, (4) similar
customer service goals and focus, (5) integration of key processes, (6) long-term
relationships, and (7) interfunctional coordination. Though also based on the
previously identified key characteristics of SCM – namely cooperation between
supply chain partners, systemic process integration, and customer focus – Mentzer
et al.’s model provides a greater level of detail than other models. It does not,
however, explicitly address the role of IT in SCM.132 In a follow-up study, Min
and Mentzer conceptualized their model by developing and testing corresponding
constructs and found support for their hypothesized relationship between supply
chain orientation, SCM, and performance.133

128 Cf. Bask, Anu H. and Jari Juga: Semi-integrated supply chains: Towards the new era of

supply chain management, in: International Journal of Logistics: Research and

Applications, Vol. 4 (2001), No. 2, pp. 137-139.
129 See Mentzer et al.: Defining supply chain management, pp. 1-25.
130 Cf. Mentzer et al.: Defining supply chain management, p. 7.
131 Cf. Mentzer et al.: Defining supply chain management, pp. 12-15.
132 For example, Tan provides a related description of effective SCM but uses only some

elements of Mentzer et al.’s model. Tan’s model does provide indications on the

usefulness of IT in SCM, see Tan: A framework of supply chain management literature,

pp. 44-45.
133 Cf. Min, Soonhong and John T. Mentzer: Developing and measuring supply chain

management concepts, in: Journal of Business Logistics, Vol. 25 (2004), No. 1, pp. 63


99.

Improving Supply Chain Performance

The principle of SCM as a more outcome-driven description has been defined
by Christopher along the following “4Rs”: (1) responsiveness, (2) reliability,

(3) resilience, and (4) relationships. Responsiveness refers to demand-driven
operations in contrast to forecast-driven operations. Reliability aims at visibility
and process control. Resilience addresses the ability to deal with unexpected
disturbances. Relationships are expected to seek win-win situations and process
integration. Thus, Christopher, too, has captured the major SCM blocks of
customer focus, process orientation, process integration, and cooperation. With the
element of resilience, Christopher has pointed out an increasing importance of
flexibility, or at least the need to be prepared for flexibility in addition to other
views.134
Packaging SCM in different terms, Lee has posited the “triple-A supply chain”
based on case studies and consulting projects conducted.135 According to Lee,
successful supply chains are characterized by agility, alignment, and adaptability.
The essence of the three “A”s fits well with principles and elements identified by
other researchers. Agility promotes the information flow with business partners,
the development of collaborative relationships, and a design for postponement in
order to combine efficient production processes without “pushing” expensive
finished good inventories down the supply chain.136 Therefore, it emphasizes
cooperative relationships and process integration across the supply chain.
Alignment is achieved by open information and knowledge exchange, the
assignment of clear roles and responsibilities across the supply chain, and the
equal sharing of risks, costs, and rewards for improvements. Again, cooperative
relationships are indicated. Adaptability refers to the readiness to adapt to changes
in the environment. In order to adapt in a timely way, companies should monitor
the environment, technology cycles, the product life cycle and ultimate customers
need, be able to develop new suppliers, and have a flexible product design. Here, a
customer focus in the form of a broader market view is suggested. All in all, Lee
has suggested an even more flexible setup than, for example, Christopher did with
the element resilience.137

134 Cf. Christopher: Logistics and supply chain management: Creating value-adding

networks, pp. 38-40.
135 See Lee, Hau L.: The triple-a supply chain, in: Harvard Business Review, Vol. 82

(2004), October, p. 105.
136 For a brief description on push and pull processes, see e.g. Chopra, Sunil and Jan A.

Van Mieghem: Which e-business is right for your supply chain? in: Supply Chain

Management Review (2000), July/August, p. 35.
137 See Lee: The triple-a supply chain and Christopher: Logistics and supply chain

management: Creating value-adding networks, pp. 39-40.


Supply Chain Management as a Strategic Option for Companies

A more recent framework of SCM has been provided by Chen and Paulraj.138
They have defined an “outside” model that influences an “inside” model. This
“inside” model consists of practices in buyer-supplier relationships.139 Though
they have explicitly focused on this dyad, they claim that their model is still
targeted at SCM as supply chains consist of many such dyadic relationships within
the network. The “outside” model consists of environmental uncertainty, customer
focus, top management support, competitive priorities, IT, and strategic
purchasing. These factors are seen as determinants of the “inside” SCM model,
which consists of practices for supply management, i.e. the buyer-supplier
relationship, the supply network structure, and logistics integration. Non-power
based, cooperative relationships are defined as beneficial supply network
structures for supply management. High logistics integration is based on logistics-
related communication and coordination between companies. The supply
management domain itself is characterized by the following practices: (1)
communication, (2) supplier base reduction, (3) long-term relationships,

(4) supplier selection, (5) supplier certification, (6) supplier involvement, (7)
cross-functional teams, and (8) trust and commitment. This “inside” model is then
hypothesized to have a positive effect on supplier performance and buyer
performance. Based on an empirical study, they have concluded “that the
theoretical constructs developed have an acceptable criterion-related validity.”140
Unfortunately, they only tested their constructs against buyer operational
performance by means of a Pearson’s correlation. Thus, only limited empirical
support can be construed from their study.
Comparing Chen and Paulraj’s model with others provides some additional
insights. Most importantly, though the model covers all the factors others derived
as well, it lacks a sound structure. For example, whereas environmental
uncertainty represents an exogenous condition, customer focus, and top
management support represent policies and attitudes. Furthermore, in the “inside”

138
Chen and Paulraj describe their SCM model in two different publications and slightly
differently in each. The one mainly referred to here is Chen and Paulraj: Towards a
theory of supply chain management: The constructs and measurements, pp. 119-150.
The other one can be found in Chen, Injazz J. and Antony Paulraj: Understanding
supply chain management: Critical research and a theoretical framework, in:
International Journal of Production Research, Vol. 42 (2004), No. 1, pp. 131-163.

139
The terms “outside” and “inside” are used because Chen and Paulraj depict their model
in such a way that some parameters and elements influence supply network structure,
buyer-supplier relationships, and logistics integration in a unidirectional way. The
nature of this influence, however, is unclear at this point. The “inside” elements could
be determinants of the “outside” model. More specifically, do they determine, affect, or
support the “ouside” model or are they determined by them?

140 Chen and Paulraj: Towards a theory of supply chain management: The constructs and
measurements, p. 134.


Improving Supply Chain Performance

model, practices are also mixed with characteristics, such as cross-functional
activities, trust, and commitment. Consequently, the approach of Mentzer et al. to
differentiate between supply chain antecedents, supply chain orientation, and
SCM appears much more appropriate. Flexibility, indicated by environmental
uncertainty and the role of IT, in SCM could be added to the set of supply chain
antecedents and the supply chain model respectively, since they are also seen as
important factors by others as pointed out above.141

Although English can be considered to be the lingua franca in the field of
Operations Management and SCM, some interesting views on SCM have been
provided by the German language literature. The principles of SCM according to
Werner are: (1) compression, i.e. a reduction in nodes and participants,

(2) cooperation, (3) integration, (4) virtualization, i.e. little legal or formal
connection within the supply chain, (5) customer orientation, (6) standardization,
and (7) optimization, i.e. analytical and operations research methods for intercompany
optimization models.142 A more aggregated view has been adopted by
Busch and Dangelmaier. Although they do not elaborate, they have categorized
SCM along the following four dimensions: (1) developments in IT, such as
Computer Integrated Manufacturing (CIM), Electronic Data Interchange (EDI),
and Enterprise Resource Planning (ERP), (2) industry specific initiatives, such as
Efficient Consumer Response (ECR), and Collaborative Planning, Forecasting and
Replenishment (CPFR), (3) partnerships, i.e. business partnering, collaboration,
and vertical integration, and (4) functional concepts, such as logistics, value chain
management, or logistics management.143
Kuhn and Hellingrath have rested SCM on three pillars: integration, process
redesign, and application of IT systems. The main prerequisites for achieving
close cooperation or integration of all partners are partnerships characterized by
trust and a common, process-oriented understanding of the supply chain. The
redesign of core business processes should lead to the effective design of material
and information flows that eliminate non-added value activities and improve
existing processes across companies. The application of IT systems has been seen
to perform two fundamental functions: coordination and communication. Its main

141
For flexibility considerations, see Christopher: Logistics and supply chain management:
Creating value-adding networks, pp. 39-40; and Lee: The triple-a supply chain, pp. 107


110. For the consideration of IT in SCM, cf. Lee: The triple-a supply chain, pp. 44-45.
142 Cf. Werner: Supply Chain Management: Grundlagen, Strategien, Instrumente und
Controlling, p. 12.
143
Cf. Busch and Dangelmaier: Integriertes Supply Chain Management -ein
koordinationsorientierter Überblick, pp. 7-8.


Supply Chain Management as a Strategic Option for Companies

problem within the SCM conceptualization has been seen to be the reluctance to
share information, due to lack of trust or misuse of trust.144

A similarly strong emphasis on long-term cooperation has been expressed by
Schönsleben. Three elements of SCM have been identified: (1) supply chain
structure, which includes the network configuration, an agreement of leadership in
the collaboration, and trust building, (2) supply chain organization, which
encompasses a sense of supply chain responsibility, process design, performance
evaluation, and information and communication, and (3) IT, which refers to state-
of-the art IT technology, such as supply chain software, e-marketplaces, or XML
and the Internet.145

Though all previously mentioned interpretations of SCM in the German
language literature have captured important aspects of SCM also identified by
international authors, their content is still fragmented. In an attempt to better
organize SCM elements, Stadtler has proposed the “House of SCM”.146 As the
foundations of SCM, Stadtler has identified functional areas such as logistics,
marketing, and operations. The framework then consists of the two major blocks
integration and coordination. The integration block comprises the choice of
partners, network organization and inter-organizational collaboration, and
leadership. Coordination, on the other side, is realized by the use of information
and communication technology, a process orientation, and advanced planning
capabilities. Special emphasis has been placed on process orientation as it aims at
coordinating all activities involved in the customer order fulfillment process and
this in turn promises the most significant impact on costs, quality, and time
performance measures.147 Customer service as a means to the ultimate goal of
competitiveness is placed on top of the two building blocks and aims to provide
guidance to all elements of the “House of SCM”.

3.c. A Third Generation Model for Supply Chain Management
The previously described models and interpretations of SCM are of relevance for
SCM theory development and have been prominently positioned in literature. A
closer analysis of these models shows that some SCM approaches are focusing on
principles, while others try to break the notion down to a more detailed level. Most
of the models described can be labeled “second generation” models of SCM, as
they build on a previously more scattered and vague understanding of SCM and
aim at bringing it together.

144 Cf. Kuhn and Hellingrath: Supply Chain Management: Optimierte Zusammenarbeit in

der Wertschöpfungskette, pp. 22-31.
145 Cf. Schönsleben: Integrales Logistikmanagement: Planung und Steuerung der

umfassenden Supply Chain, p. 85.
146 Cf. Stadtler: Supply chain management - an overview, pp. 9-10.
147 Cf. Stadtler: Supply chain management - an overview, p. 16.


Improving Supply Chain Performance

Though these second generation models of SCM are better organized and more
well-grounded on existing literature than “first generation” models, it still appears
that there is currently no single model that offers an overall sound and convincing
structure. In order to derive such a model, researchers have suggested structuring
the research of the SCM field differently. Most propose a three-level structure.
Giannakis and Croom have suggested the three categories synthesis, synergy, and
synchronization.148 Stevens has considered a strategic perspective, a tactical
perspective, and an operational perspective.149 Göpfert has used the terms
normative SCM, strategic SCM, and operative SCM.150 Though all three share a
similar content in their categories, Göpfert’s terminology seems to be the most
appropriate one. According to Göpfert, the normative level represents a meta-level
of SCM. It fulfills the function of giving identity, motivation, direction, and focus
to the SCM concept. The strategic level includes the structure of the supply chain
network. On the operational level, the execution of the supply chain strategy is
described.

Based on the second generation SCM models and the research structure
proposed by Göpfert, the core SCM concept should be positioned on the strategic
level. Based on the previously reviewed models, a “third generation” SCM
framework is developed. Figure B-6 provides an overview. Its main contribution
is that it covers all SCM aspects identified so far and combines them in one sound
model. It is intended to avoid incompleteness and inconsistencies on different
levels. Whereas completeness is hard to claim, the basic structure is believed to be
sound and consistent and prepared for extensions if appropriate.

148
Cf. Giannakis, Mihalis and Simon R. Croom: Toward the development of a supply
chain management paradigm: A conceptual framework, in: Journal of Supply Chain
Management, Vol. 40 (2004), No. 2, Spring, p. 32.

149
Cf. Stevens: Integrating the supply chain, pp. 4-5.

150 Cf. Göpfert: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain
Managements, pp. 39-43.


Supply Chain Management as a Strategic Option for Companies

Normative Level

Customer orientation

Cooperative orientation

Systemic, holistic view of supply chains
Process orientation
Contingency approach

Strategic Level

SCM antecedents (strategic attitudes)

Core SCM model:

Prerequisite strategic

Strategic physical

SCM Cooperation

management decisions

and technical tools /
infrastructure

-competitive priorities

Coordination Collaboration

-SCM processes

-e-business
-supply chain structure


-location and
facilities

Integration

Operational Level

Physical and technical Managerial and behavioral
management components management components

Figure B-6: A holistic third generation SCM framework151

The model follows a top-down approach. Five meta-policies are placed on the
normative level, since these policies provide the umbrella for the strategic as well
as the operational levels. Four of them can be derived directly from the previous
discussion. These are customer orientation, cooperative orientation, process
orientation, and a systemic view of supply chains. Without these four elements, no
viable SCM application is possible. The fifth element – contingency – has not
been discussed to a great extent in the SCM literature. It calls for
“appropriateness” in the way supply chains are designed and managed.152 In that
light it reflects the fact that not all relationships within a supply chain require the
same level of cooperation. Thus, the SCM model defined on the strategic level
represents the dimensions from which to choose in implementing SCM. It is,

151
A more detailed graphic can be found in Appendix 1.

152
See Cox, Andrew: Power, value and supply chain management, in: Supply Chain
Management: An International Journal, Vol. 4 (1999), No. 4, pp. 171-173; Cox,
Andrew: The power perspective in procurement and supply management, in: The
Journal of Supply Chain Management, Vol. 37 (2001), No. 2, Spring, pp. 4-5; and
section B.IV. in this text, where challenges for SCM are discussed.


Improving Supply Chain Performance

however, assumed that especially these dimensions hold hidden opportunities to
improve supply chain performance in existing non-transactional supply chain
relationships. In the context of high performance manufacturing practices,
Schroeder and Flynn refer to this concept of appropriateness as a contingency
approach.153 This term has been adopted in the SCM framework depicted in
Figure B-6. On the normative level, no direct rules are implied. For example, it is
not being claimed that only cooperative policies are successful, but rather that
there should exist an overall organizational direction towards cooperative
behavior. The same holds true for the other three elements, i.e. customer
orientation, process orientation, and systemic view of supply chains.

Since SCM is considered to be a strategic concept, the core model is placed on
the strategic level. In order to avoid inconsistencies, the elements are carefully
separated. Though of crucial importance, competitive priorities, supply chain
structure, and SCM processes are not included in the core SCM model. They are
considered to be interdependent with the core model but, based on the focus of
SCM, are external to it, as illustrated in Figure B-6. Competitive priorities focus
on the strategic position of a company and supply chain structure takes into
account the structural interrelations. SCM processes are supplier relationship
processes, internal supply chain processes and customer relationship processes154
or more specifically the ones Lambert, Cooper, and Pagh have defined.155 SCM
processes also include industry-specific process packages such as Efficient
Consumer Response (ECR) or Collaborative Planning, Forecasting and
Replenishment (CPFR).

The essence of the framework is SCM cooperation. This comprises the
elements coordination, collaboration, and integration. Because of their relevance,
these elements are discussed in greater detail in section B.II. In short, coordination
is mainly concerned with communication and information sharing functions.
Collaboration requires a greater level of interaction and involvement and includes
joint activities and teamwork. Integration aims at reducing frictions between
interacting and communicating entities. Details are shown in a more precise
illustration in Appendix 1.

153
Cf. Schroeder, Roger G. and Barbara B. Flynn: High performance manufacturing: Just

another fad?, in: Schroeder, Roger G. and Barbara B. Flynn (Eds.): High performance

manufacturing - global perspectives, New York 2001, pp. 4-5.
154 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 17.
155
Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 8-9, i.e. customer relationship management, customer
service management, demand management, order fulfillment, manufacturing flow
management, procurement, product development and commercialization, and returns
and reverse logistics.


Supply Chain Management as a Strategic Option for Companies

Excluded from the core model are strategic physical and technical tools and
infrastructure. They are available to design and shape coordination, collaboration,
and integration, but are not considered to be means in themselves. As the core
SCM model consists of managing elements, e-business is seen as a set of tools
supporting, advancing and enabling the practices of the core model. They are
discussed in greater detail in section B.III.3. as they are seen to be of great
importance for SCM. Other more traditional elements are location and facility
considerations.

Another category identified on the strategic level is strategic attitudes. Mentzer
et al. have used the term SCM antecedents.156 These are attributes that make SCM
more effective if present or hinder the effectiveness if absent. The existing
literature especially emphasizes the importance of trust, and commitment.157 Other
antecedents are top management support, common supply chain understanding,
acknowledgment of interdependencies, risk and reward sharing, accountability and
responsibility, and willingness of supply chain alignment.

On the operational level, the categories suggested by Lambert, Cooper, and
Pagh are proposed to structure operational management components.158 In
essence, the operational level is concerned with the execution of the strategic
level. Suggestions made by other authors fit in well with the components
identified by Lambert, Cooper, and Pagh. Therefore, their components can be
adopted without the need to add many specific suggestions made by other
authors.159 Some elements here can be considered to be more strategic in nature, as
for example culture and attitude. The components identified on the operational
level, however, refer to the operational implementations of these. Otherwise,
adjusted versions are considered separately on the strategic and normative levels.
A common supply chain understanding or a customer orientation would be
specific instances of cultural considerations.

156
Cf. Mentzer et al.: Defining supply chain management, pp. 12-15.

157
See for example Mentzer et al.: Defining supply chain management, pp. 12-13;
Spekman, Robert E., John W. Kamauff Jr. and Niklas Myhr: An empirical investigation
into supply chain management: A perspective on partnerships, in: Supply Chain
Management: An International Journal, Vol. 3 (1998), No. 2, pp. 55-56; Chandra and
Kumar: Supply chain management in theory and practice: A passing fad or a
fundamental change?, pp. 101-103; and Chen and Paulraj: Towards a theory of supply
chain management: The constructs and measurements, pp. 149-150.

158
Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 11-12.

159
For example Kanji and Wong: Business excellence model for supply chain
management, pp. 1153-1155 especially point out management by fact and continuous
improvement practices. As these represent management methods, they can be listed as
such as managerial management components under Lambert, Cooper, and Pagh’s
categories. For an overview of these categories, see Appendix 1.


Improving Supply Chain Performance

In a slightly different approach, Trent has related SCM to the principles of
TQM. Trent has argued that, although many companies claim to apply TQM, they
do not actually live by and practice them.160 This view has also been taken by
Hayes and Pisano, who remarked that instead of focusing on the form of
organizational methods such as TQM, it is necessary to focus on the substance, i.e.
the underlying skills and capabilities, promoted by these concepts.161 Reviewing
the principles of TQM reveals the proximity to SCM. Trent has identified the
following eight principles of TQM.162 (1) Define quality in terms of customers and
their requirements. (2) Pursue quality at the source. (3) Stress objective rather than
subjective analysis. (4) Emphasize prevention rather than detection of defects. (5)
Focus on process rather than output. (6) Strive for zero defects. (7) Establish
continuous improvement as a way of life. (8) Make quality everyone’s
responsibility.

Viewing the third generation SCM framework derived in this text with these
principles indeed shows the complementary nature of the TQM concept and SCM.
The first principle directly relates to the customer orientation of SCM. The second
one is reflected by the systemic and holistic view of supply chains. This view also
covers the fourth and eighth principle of TQM. Management by objective rather
than subjective justifications is incorporated as strategic attitude and as the
operational management component. Process focus is another major component of
SCM. Striving for zero defects is quite specific to TQM, but can be related to the
aim of maximal efficiency under the conditions determined by strategic decisions.
Establishing continuous improvement can be seen as central to collaboration,

160
Cf. Trent, Robert J.: Applying TQM to SCM, in: Supply Chain Management Review
(2001), May/June, p. 71; and Bragg, Wayne: Executive commentary on Liedtka's
article: Collaboration across lines of business for competitive advantage, in: Academy
of Management Executive, Vol. 10 (1996), No. 2, pp. 35-36.

161
Cf. Hayes, Robert H. and Gary P. Pisano: Beyond world-class: The new manufacturing
strategy, in: Harvard Business Review, Vol. 72 (1994), January/February, p. 78.

162
Cf. Trent: Applying TQM to SCM, p. 71. Trent’s eight principles differ slightly from
the eight principles of quality management that underlie the ISO 9000 standards, see
n.a.: International Organization of Standardization: http://www.iso.org, 2005, retrieved
on: February 15, 2006. These, as well, are based on the previous work of quality
management authors like Ishikawa, Feigenbaum, Deming, Juran, Crosby, Imai, Ohno
and Taguchi. The ISO 9000 quality principles comprise customer focus, leadership,
involvement of people, process approach, system approach to management, continual
improvement, factual approach to decision making and mutually beneficial supplier
relationships. Leadership and a system approach to management are not reflected in
Trent’s principles. Instead, Trent adds a prevention emphasis and the aim for zero
defects.


Supply Chain Management as a Strategic Option for Companies

because this is in fact why companies do collaborate.163 The last principle,
establishing a holistic attitude of responsibility and accountability, is part of the
SCM antecedents.

Since the SCM framework depicted in Figure B-6 and illustrated in more
detail in Appendix 1 is sound, consistent, and comprehensive in light of the
previous SCM concept expositions, it will be pursued in the following as the
relevant SCM foundation. Figure B-7 illustrates the organization of the discussion
in this text.

Strategic Level
Strategic physical
and technical tools /
infrastructure
Prerequisite strategic
management decisions
Core SCM model:
SCM Cooperation
E-Business
B.III.3
Location and Facilities
SCM antecedents (strategic attitudes)
Coordination Collaboration
Integration
B.II.
Strategic Fit
B.III.1.
Processes and
Structures
B.III.2
Figure B-7: Overview of SCM theory discussion structure

Section B.II. discusses the core SCM model, i.e. the elements of SCM
cooperation: coordination, collaboration and integration. In sections B.III.1 and
B.III.2., the necessary prerequisite strategic management decisions in the context
of SCM are addressed. More concretely, the often noted strategic fit and how this
is reflected in the newly developed framework is discussed. Related to structural
aspects of SCM, then the process view of SCM is emphasized. Because of its
increasing importance and acceptance, the Supply Chain Operations Reference
(SCOR) model is next reviewed and placed in perspective relative to the SCM
framework. Based on this discussion of supply chain structures and processes, a
formal framework is developed. Such a formal representation is believed to better
support the analytical analysis of supply chains. Finally, in section B.III.3., e-
business aspects are considered. Due to their rapid diffusion and potential, they are
of special importance and therefore are discussed in a broader perspective.

163
Therefore, this is added as part of the managerial and behavioral management
components based on the TQM concept.


Improving Supply Chain Performance

II. Cooperation as Success Factor for Supply Chain Management
1. The Bullwhip Effect as a Result of Uncoordinated Decision Making
Forrester has been the first to identify the phenomenon of oscillating and
amplifying order behavior upstream of supply chains and its effects on
inventories, capacity utilization and other operational parameters.164 This Forrester
effect has become known as the bullwhip effect and can be considered to be the
best-known phenomenon of supply chain inefficiencies. According to Lee, the
first time the bullwhip effect was evident in an industrial company was in the
supply chain of Procter & Gamble’s diaper products. Though diaper sales were
relatively stable, fluctuations of distributor orders were much higher and so were
material orders of Procter & Gamble’s suppliers.165 After this discovery, the same
effect has been observed in other supply chains as well and is still evident.166 The
bullwhip effect is evidence of the consequences of uncoordinated decision
making, i.e. that members of supply chains make decisions without having
knowledge about the decisions in other parts of the supply chain. The resulting
order fluctuations have a variety of consequences for the supply chain. These
fluctuations increase manufacturing costs, inventory costs, replenishment lead
times, transportation costs, and labor costs for shipping and receiving.
Additionally, the level of product availability decreases and relationships across
supply chains are affected negatively.167

The structure of a system is of great importance for explaining system
behavior. This bullwhip effect is a consequence of this structure.168 Structure
influences the behavior of a system to a great extent. More precisely, feedback
structures and inherent delays unavoidably cause distortions that then become

164
Cf. Forrester: Industrial dynamics: A major breakthrough for decision makers, pp. 37


66.
165 Cf. Lee, Hau L., V. Padmanabhan and Seungjin Whang: The bullwhip effect in supply

chains, in: Sloan Management Review, Vol. 38 (1997), No. 3, Spring,

pp. 93-94.
166 Cf. McCullen, Peter and Denis R. Towill: Diagnosis and reduction of bullwhip in

supply chains, in: Supply Chain Management: An International Journal, Vol. 7 (2002),

No. 3, p. 164; and Lee, Padmanabhan and Whang: The bullwhip effect in supply

chains, p. 93 who report about similar effects in HP’s printer supply chain.
167 Cf. Andraski, Joseph C.: Leadership and the realization of supply chain collaboration,

in: Journal of Business Logistics, Vol. 19 (1998), No. 2, p. 10; and Chopra and Meindl:

Supply chain management: Strategy, planning, and operation, pp. 480-481.

168
Cf. Forrester, Jay W.: Industrial dynamics - after the first decade, in: Management
Science, Vol. 14 (1968), No. 7, p. 406.


Cooperation as Success Factor for Supply Chain Management

evident through oscillations in key system parameters, such as inventory levels or
utilization rates.169

Based on a more detailed analysis of given industry supply chain structures,
Lee, Padmanabhan, and Whang have identified four factors that cause the
bullwhip effect: (1) demand forecast updating, (2) order batching, (3) price
fluctuation, and (4) the rationing and shortage game. These will be described
briefly in the following:

-
Demand forecast updating. When performing demand forecasts,
companies interpret historical order information and update them
regularly. This order information from customers, however, does not
directly reflect actual demand. This information is used to determine
supply requirements as a function of historical demand information,
service level policies, and lead times in order to satisfy future demand and
safety stocks. The further upstream in the supply chain these forecasts are
conducted the more their variability increases.170 Because longer lead
times require higher safety stocks under otherwise identical conditions,
worsening the bullwhip effect, some authors mention long lead times as a
separate reason for the bullwhip effect.171

-
Order batching. Two forms of order batching are identified by Lee,
Padmanabhan, and Whang: periodic ordering and push ordering. Most
frequently, periodic orders are used. Many companies run their MRP
systems or inventory status periodically and therefore, orders occur
periodically as well. Additionally, fixed order costs, such as order
processing costs and transportation costs, contribute to larger orders in
order to reduce per unit order costs. Push ordering refers to behavioral
order distortions. It occurs in cases of budget spending related end-of-year
or end-of-period surges or forward ordering by sales agents in order to
meet incentive related goals.172 It also contributes to erroneous demand
signaling and therefore less reliable forecasts upstream in the supply
chain.

-
Price fluctuation. Temporary price discounts, promotions, and payment
term benefits offered by manufacturers, wholesalers, or distributors to
downstream supply chain members encourages forward buying behavior.

169
Cf. Maier, Frank: Die Integration wissens- und modellbasierter Konzepte zur
Entscheidungsunterstützung im Innovationsmanagement, Berlin 1995, pp. 177-178.

170
Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains, p. 95.

171
Cf. for example Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the
supply chain: Concepts, strategies, and case studies, pp. 103-104 who add long lead
times as a fifth reason for the bullwhip effect.

172
Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains, pp. 95-96.


Improving Supply Chain Performance

In order to benefit from these price reductions, companies buy larger
amounts than immediately needed. Depending on inventory holding costs,
this might be beneficial for really large amounts. In any case, for
upstream supply chain members it is impossible to derive real customer
demand because of this forward buying behavior. Higher direct costs
might occur because of over-utilization of resources and resulting
negative long-term consequences of varying capacity utilization.173

-
Rationing and shortage game. If supply is limited due to a temporary
surge in demand and orders are only partly filled due to this shortage,
customers might react by overstating their real demands in order to
receive a larger share of the limited supply. When demand returns to
normal levels, orders are cancelled or, because of previous more-thandemanded
deliveries, simply disappear. This is especially a problem when
customers only anticipate a shortage and place multiple orders with
multiple suppliers. Then, after the first order is fulfilled, all redundant
orders are cancelled. The problem is that it is almost impossible for a
manufacturer to tell real orders from fake ones.174 As Sterman remarked:
“Even a perfect forecast will not prevent a manager who ignores the
supply line from overordering.”175

If one common denominator can be derived as counter-measure for the
bullwhip effect, it would be coordination. Based on simulation results, Towill has
concluded that the improvements gained from information integration and
therefore information sharing and information exchange are relatively high.176
Operational and economic factors, such as lead times and ordering costs, also play
a role but the lack of coordination seems to explain most of the bullwhip effect.
Though coordination can significantly reduce the bullwhip effect, it may not
completely eliminate it.177 The magnitude of the bullwhip effect is highly
dependent on the specific problem situation and therefore hard to pin down in
general terms. The major causes and counter-measures, however, are well known

173 Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains, p. 97.
174 Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains, pp. 97-98.
175 Sterman, John D.: Modeling managerial behavior: Misperceptions of feedback in a

dynamic decision making experiment, in: Management Science, Vol. 35 (1989), No. 3,

p. 336.
176 Cf. Towill, Denis R.: The seamless supply chain - the predator's strategic advantage, in:
International Journal of Technology Management, Vol. 17 (1997), No. 1, p. 50.
177
Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply
chain: Concepts, strategies, and case studies, p. 109.


Cooperation as Success Factor for Supply Chain Management

and grounded on the foundations laid out by Forrester as well as Lee,

Padmanabhan, and Whang .178
Counter-measures to weaken or even eliminate the bullwhip effect have been

analyzed and suggested by several authors. They can be summarized as follows:

-
Information sharing. In order to avoid the problem of multiple demand
forecasts based on indirect demand data, it is suggested that end consumer
demand information be shared with upstream members of the supply
chain. Still, differences in the forecasts might occur due to different
forecasting methods and assumptions. The concept of Vendor Managed
Inventory (VMI) builds on information sharing but goes one step further.
With VMI, suppliers or manufacturers manage inventory directly at the
retailer’s site. Inventory information is shared in addition to demand
information. Improvements in automation and information technology
have been important for efficiently managing such a system.
Operationally, shorter lead times reduce uncertainty.179 Consequently,
safety stock inventory and capacity cushions can be reduced. Information
sharing can also include capacity information sharing with downstream
supply chain partners. Fundamentally, information sharing influences all
causes for the bullwhip effect positively.180

-
Smaller order batches. The effects of large order batches contribute not
only to wrong demand signaling but also to increase in workload
fluctuations. Besides more frequent MRP runs and policy adjustments to
avoid push ordering, operational improvements are important to keep per
unit costs low even with small order batches.181 This can be achieved by
transportation aggregation through third party logistics providers or
arrangements with cosuppliers182 and by reduction of order processing
costs through automation and ERP systems.

178
Cf. Sahin, Funda and E. Powell Robinson Jr.: Flow coordination and information
sharing in supply chains: Review, implications, and directions for future research, in:
Decision Sciences, Vol. 33 (2002), No. 4, Fall, pp. 511-514.

179
Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains, pp. 98-100.

180
Cf. Lee, Hau L., V. Padmanabhan and Seungjin Whang: Information distortion in a
supply chain: The bullwhip effect, in: Management Science, Vol. 43 (1997), No. 4,
April, p. 558.

181
Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains,
pp. 100-101; and Chopra and Meindl: Supply chain management: Strategy, planning,
and operation, pp. 490-492.

182
For the term cosupplier as suppliers who deliver to the same customer, cf. Hammer,
Michael: The superefficient company, in: Harvard Business Review, Vol. 79 (2001),
September, pp. 88-89.


Improving Supply Chain Performance

-
Price stability. Instead of providing irregular price discounts, an every
day low price policy can avoid forward buying or purchase postponement
in anticipation of price discounts or promotions.183 Another alternative is
to move from lot size-based discounts to volume-based quantity
discounts.184

-
Reducing delays. Material flow delays, information flow delays, and
information distortion can be reduced by eliminating entire tiers from the
supply chain or by time compression of the processes.185 Changing the
supply chain structure, however, is a difficult task. Therefore, time
compression is the more common and more feasible approach for
counterbalancing the bullwhip effect.

As pointed out before, the bullwhip effect can be mainly attributed to a lack of
coordinated decision making. This includes structural deficits with regard to
coordinated decision making. In the context of SCM, the terms cooperation,
collaboration, and integration appear frequently together with or instead of
coordination. Therefore, the next section takes a closer look at those terms and
examines how they correspond, interrelate, and most importantly, differ.

2.
Supply Chain Management Cooperation: Coordination, Collaboration, and
Integration at the Core
Generally, coordination and coordinated decision making refers to separated
entities that work together for decision alignment in order to improve overall
performance. This has been a major issue of early economic theory that
differentiated between the firm and its hierarchies and price mechanisms as forms
of coordination.186 If separate companies coordinate, Coase has referred to that as
combination or integration.187 In the context of management research and in
particular SCM research, the related terms cooperation, coordination, and
collaboration are often used interchangeably without clearly distinguishing them
from each other. This can cause confusion and ambiguity.

183 Cf. Lee, Padmanabhan and Whang: The bullwhip effect in supply chains, p. 101.
184 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

pp. 487-493.
185 Cf. Towill: The seamless supply chain - the predator's strategic advantage, p. 51.
186 Cf. Coase, Ronald H.: The nature of the firm, in: Economica, Vol. 4 (1937), No. 13-16,

pp. 7-11; and Williamson, Oliver E.: Economic organizations: Firms, markets and
policy control, Bodmin 1986, pp. 32-36.
187 Cf. Coase: The nature of the firm, pp. 14-15.


Cooperation as Success Factor for Supply Chain Management

Cooperation is defined as acting or working together for a shared purpose,188
working or acting together toward a common end or purpose, being compliant,189
or as working with someone toward a common goal.190 In the context of SCM,
Quiett has interpreted cooperation as “little more than toleration of each other.”191
While this view might be a bit too drastic, the other definitions imply that
cooperation emphasizes mainly the alignment towards a common goal and a
shared purpose. The notion of “working together” in the context of cooperation
does not suggest a close operational working relationship, but rather a positive
attitude towards each other.

Coordination refers to a more direct, active cooperation. It is defined as “the
act of making arrangements for a purpose,” the “harmony of various elements,”192
“harmonious adjustment or interaction,”193 and making separate things working
together.194 Compared to cooperation, coordination indicates an interactive, joint
decision making process, where separate entities influence each others’ decisions
more directly. Besides horizontal coordination, i.e. coordination within a supply
chain tier, and vertical coordination, i.e. coordination across supply chain tiers, for
example between supplier and customer, coordination can also be distinguished
according to the mechanism of coordination. The fundamental mechanisms are
markets and hierarchies. Both mechanisms can reflect different degrees of
coordination. Market structures refer mainly to incentive-driven coordination
between separate, legally independent companies whereas hierarchical structures
indicate either a high unilateral dependency or that companies are not legally
independent and equity is shared.195 High degrees of coordination are subject to
antitrust actions because they are believed to impede competition and reduce

188
Cf. Cambridge Dictionaries Online, http://dictionary.cambridge.org/, Cambridge
University Press,retrieved on: February 15, 2006.

189
Cf. The American Heritage Dictionary of the English Language,
http://www.bartleby.com/61/, Houghton Mifflin Company,retrieved on: February 15,
2006.

190 Cf. Heinle's Newbury House Dictionary of American English, http://nhd.heinle.com,
Thomson Heinle,retrieved on: February 15, 2006.
191 Cf. Quiett, William Frank: Embracing supply chain management, in: Supply Chain

Management Review (2002), September/October, p. 45.
192 Heinle's Newbury House Dictionary of American English.
193 The American Heritage Dictionary of the English Language.
194 Cf. Cambridge Dictionaries Online.
195 Williamson introduces the hybrid form as another governance structure, positioned

between markets and hierarchies, see Williamson, Oliver E.: Comparative economic
organization: The analysis of discrete structural alternatives, in: Administrative Science
Quarterly, Vol. 36 (1991), No. 2, pp. 269-296.


Improving Supply Chain Performance

welfare. Whether or not this belief is true has been the subject of discussions
among economists and has been doubted especially for vertical coordination.196

Collaboration is defined as “[working] together or with someone else for a
special purpose,”197 “[working] together, especially in a joint intellectual
effort,”198 or simply as working with someone.199 In the last instance, collaboration
is simply defined as a synonym for working together. The other two definitions
point out common objectives and efforts. Therefore, they put the activity in a
context. Whereas coordination is mainly conducted by sending the right signals or
sharing the right information and the same policies, collaboration indicates a joint,
interactive process that results in joint decisions and activities. By that, it also
indicates a higher degree of joint implementation and can be thought of as a
teamwork effort. According to this interpretation, coordination alone excludes
joint implementation and operational efforts.

Within the SCM framework, the core SCM model is labeled SCM
cooperation.200 It is seen as a strategic directive that subsumes coordination and
collaboration. The distinction between these two is necessary in order to
distinguish different types of cooperation that are relevant to SCM. Cooperation
can be divided into intra-company cooperation, bilateral cooperation, and
multilateral cooperation, depending on the scope of the cooperation under
consideration.201

In terms of cooperative intensity, collaboration can be seen as more intensive
than coordination because most of the time it subsumes all characteristics of
coordination as well. Therefore, in a hierarchy of different levels of cooperation,
collaboration would be positioned above coordination. This is not to say that
coordination is less important or relevant; it is just not as intensive.

In the context of SCM, coordination aims at achieving global optimization
within a defined supply chain network. Interactive, joint collaborative efforts aim
to exploit hidden potential and consequently expand the optimization potential, i.e.
shifting the efficient performance frontier upwards. This view is also supported by
Shaw, who has differentiated between three types of coordination in terms of level

196 For a review of antitrust, see Williamson: Economic organizations: Firms, markets and

policy control, pp. 250-257; and Boarman, Patrick M.: Antitrust laws in a global

market, in: Challenge (1993), January/February, pp. 30-36. Negative welfare effects

have been already doubted by Spengler, Joseph J.: Vertical integration and antitrust

policy, in: Journal of Political Economy (1950), p. 352.
197 Cambridge Dictionaries Online.
198 The American Heritage Dictionary of the English Language.
199 Cf. Heinle's Newbury House Dictionary of American English.
200 See Figure B-6, p. 45 and Appendix 1.
201 Cf. Kuhn and Hellingrath: Supply Chain Management: Optimierte Zusammenarbeit in

der Wertschöpfungskette, pp. 38-39.


Cooperation as Success Factor for Supply Chain Management

of involvement, in ascending order: (1) simple information exchange,

(2) formulated information sharing, and (3) modeled collaboration. Simple
information exchange is straightforward in its meaning. It refers to information
exchange without additional interpretation or rules. In formulated information
sharing, such policies as restocking policies are shared together with operational
information. In modeled collaboration, operational models are also shared,
together with capabilities, factory load, inventories, and orders.202 This
understanding can be directly linked to the three levels of collaboration that Quiett
has identified, which are data exchange, cooperative collaboration and cognitive
collaboration.203 These views, however, indicate a more extensive information
sharing scheme on the highest level instead of a close, teamwork-like working
relationship.
As also suggested in the context of the bullwhip effect, supply chain
profitability as a whole can only be maximized when all stages are coordinated.204
Consequently, this must lead to concerted decisions.205 The significance of
coordination has been confirmed by a study conducted by Thonemann among
manufacturing companies. There, supply chain coordination has been identified as
the top success factor by manufacturing companies.206 Sahin and Robinson have
stated that “a supply chain is fully coordinated when all decisions are aligned to
accomplish global system objectives.”207 Information sharing is of central
importance for coordination. It allows for coordinated forecasts and forecasts
based on richer information.208

Thus, a “lack of coordination occurs when decision makers have incomplete
information or incentives that are not compatible with system-wide objectives.”209
As also shown in the context of the bullwhip effect, even full information

202
Cf. Shaw, Michael J.: Information-based manufacturing with the Web, in: The
International Journal of Flexible Manufacturing Systems, Vol. 12 (2000), No. 2,3,
April, p. 123.

203 Cf. Quiett: Embracing supply chain management, p. 45.
204 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,
pp. 46-47.
205 Cf. Christopher: Logistics and supply chain management: Creating value-adding
networks, p. 258.

206
Cf. Thonemann, Ulrich et al.: Supply chain champions, Wiesbaden 2003, p. 30.
Thonemann et al. refer to cooperation in their study, but in fact mean coordination, i.e.
sharing of information.

207 Sahin and Robinson Jr.: Flow coordination and information sharing in supply chains:
Review, implications, and directions for future research, p. 507.
208 Cf. Swaminathan, Jayashankar M. and Sridhar R. Tayur: Models for supply chains in e-
business, in: Management Science, Vol. 49 (2003), No. 10, p. 1397.
209 Sahin and Robinson Jr.: Flow coordination and information sharing in supply chains:
Review, implications, and directions for future research, p. 507.


Improving Supply Chain Performance

availability does not guarantee optimal supply chain performance. Nevertheless,
full information availability can have a significant, positive impact on supply
chain performance.210 But the problem of conflicting objective functions may
remain and cause forecasts to be distorted.211

Complementary to the counter-measures identified by Lee, Padmanabhan, and
Whang in the context of the bullwhip-effect, Chopra and Meindl have considered
five categories of obstacles to coordination. These comprise factors that lead to
local optimization, an increase in information delay, distortion, and variability
within the supply chain. These categories are:212

-
Incentive obstacles. These are obstacles that are caused by wrong
incentives provided to supply chain members in order to influence their
decisions to support global optimization instead of pareto-efficient
solutions.

-
Information processing obstacles. They consist of orders based on
forecasts instead of customer demand, and a lack of information sharing.

-
Operational obstacles. Large ordering lot requirements, rationing and
shortage gaming, and large replenishment lead times can be summarized
as operational obstacles. The effect of lead times was pointed out by Stalk
and Hout, who note that halving lead times can result in the halving of
forecast errors.213

-
Pricing obstacles. Lot sizes based on quantity discounts and price
fluctuations contribute largely to the variability within supply chains.

-
Behavioral obstacles. Policies and management practices, such as
frequency of MRP runs, limited company perspective and local
optimization characterize this category.

When examining coordination, one can distinguish between two types,
horizontal coordination and vertical coordination. Horizontal coordination refers
to coordination issues within one tier, whereas vertical coordination involves
different tiers, such as a customer and supplier. Horizontal coordination problems
are for instance location decisions and centralization decisions. Location decisions

210
Cf. Chen, Frank et al.: Quantifying the bullwhip effect in a simple supply chain: The
impact of forecasting, lead times, and information, in: Management Science, Vol. 46
(2000), No. 3, March, p. 442. Information and its value is discussed in more detail in
section B.II.3.

211 Cf. Swaminathan and Tayur: Models for supply chains in e-business, p. 1396.
212 For the following, see Chopra and Meindl: Supply chain management: Strategy,
planning, and operation, pp. 482-487.
213 Cf. Stalk, George and Thomas M. Hout: Competing against time, London 1990, pp. 31


34.

Cooperation as Success Factor for Supply Chain Management

can be analyzed within the Hotelling model, where it is implicitly assumed that
firms solve such coordination problem of their decision to the extent that they
correctly anticipate the behavior of other parties.214 Vertical coordination
problems include the well-known newsvendor problem,215 issues of risk pooling,
coordinated demand forecasting, coordinated pricing, and coordinated lot sizing.
Some of these were discussed earlier. The bullwhip effect, for example, can be
considered to be a consequence of uncoordinated demand forecasts.

Centralization, also known as risk pooling, is referred to as a horizontal
coordination mechanism. Risk pooling reduces demand variability if demand is
aggregated across locations. It is a means by which safety stock and average
inventory can be reduced in a system. Of course, some costs might increase, such
as transportation costs or customer lead time and therefore this has to be weighed
against the benefits.216 To illustrate this beneficial effect, Christopher has
described the “square root rule”. According to this, system inventory can be
reduced proportionally to the square root of the number of stock locations before
and after centralization, under certain assumptions. For example, a reduction from
25 locations to four would correspond to the relation


25 :
4 , which leads to 5:2
and therefore a 60% reduction.217
Munson, Hu, and Rosenblatt have provided examples for horizontal and
vertical coordination problems. It has been shown that in these straightforward
examples, better solutions can be derived by coordinated decision making. They
have developed several numerical examples in the areas of location decisions,
centralization, lot sizing, demand forecasting, pricing, and newsvendor lot sizing
to illustrate this.218 Often, such coordination problems refer to specific situations
on the operational level. In the context of SCM, however, the strategic dimension
of coordination is of paramount interest.

214 See Gabszewicz, Jean J. and Jacques-Francois Thisse: Location, in: Aumann, Robert J.

and Sergiu Hart (Eds.): Handbook of game theory with economic applications volume

2,Amsterdam 1994, chapter 9.
215 For example, see Eppen, Gary D.: Effects of centralization on expected costs in a multi-

location newsboy problem, in: Management Science, Vol. 25 (1979), No. 5, pp. 408


501.
216 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply
chain: Concepts, strategies, and case studies, pp. 66-67.
217 For more details, see Christopher: Logistics and supply chain management: Creating
value-adding networks, p. 215.
218
See Munson, Charles L., Jianli Hu and Meir J. Rosenblatt: Teaching the costs of
uncoordinated supply chains, in: Interfaces, Vol. 33 (2003), No. 3, May/June, pp. 24-36
for simple mathematical examples that clearly prove this. A detailed explanation would
exceed the scope of this text.


Improving Supply Chain Performance

Sahin and Robinson Jr. have summarized the major strategic and tactical
coordination mechanisms according to the following categories:219

-
Price coordination using quantity discounts. System optimization is
sought through the alignment of a manufacturer’s pricing structure with a
retailer’s purchasing incentives under a variety of conditions, such as
capacity restrictions and different information availability.

-
Non-price coordination. This includes mechanisms such as service
territories, quantity forcing, and service differentiation.

-
Buy-back and returns policy. Such strategies aim to increase stocking
incentives for retailers, especially for perishable products.

-
Quantity flexibility. Contracts including flexible quantities – such as a
guaranteed amount of minimum purchases by a buyer and maximum
amount of products made available through a supplier – aim at sharing the
risks of forecast deviations.

-
Allocation rules. Due to scarce capacity resources, retailers might distort
their orders, which in turn leads to supply chain inefficiencies. Cachon
and Lariviere have shown that under certain conditions, a supply chain is
better off not providing truthful information about actual order
requirements but also note that this might change if conditions change,
such as marginal cost for capacity or marginal retailer costs.220 In
conclusion, Cachon and Lariviere state “[…] that truth telling provides
some advantages to the supply chain that should be weighed against the
costs of inducing truth telling.”221

In collaboration, two or more entities work together, share resources, and seek
to achieve collective goals. It depends on the ability to trust each other, and to
appreciate one another’s knowledge and emphasizes the building of meaningful
relationships.222

An understanding in line with this interpretation of collaboration is provided
by Liedtka, who has defined collaboration “as a process of decision making

219
Cf. Sahin and Robinson Jr.: Flow coordination and information sharing in supply
chains: Review, implications, and directions for future research, pp. 508-509.

220
Cf. Cachon, Gérard P. and Martin A. Lariviere: Capacity choice and allocation:
Strategic behavior and supply chain performance, in: Management Science, Vol. 45
(1999), No. 8, pp. 1091-1107.

221
Cachon and Lariviere: Capacity choice and allocation: Strategic behavior and supply
chain performance, p. 1104.

222
Cf. Stank, Theodore P., Patricia J. Daugherty and Alexander E. Ellinger:
Marketing/logistics integration and firm performance, in: The International Journal of
Logistics Management, Vol. 10 (1999), No. 1, p. 12.


Cooperation as Success Factor for Supply Chain Management

among interdependent parties; it involves joint ownership of decisions and
collective responsibility for outcomes.”223 Liedtka has emphasized the cross-
functional teamwork aspect of collaboration with a clear focus on processes
instead of functions. Because processes rarely stop at company boundaries, this
includes external organizations as well. Therefore, the term partnership is also
used to include external collaboration. Success factors identified in Liedtka’s
study are quite independent from legal forms of partnerships. The components of
successful partnering comprise a partnering mindset, a partnering skillset, and a
supporting organizational architecture.224

In a Deloitte study conducted in 2003, collaboration has been characterized by
internal and external teamwork in the context of manufacturing companies, i.e.
with customers and suppliers. As differentiating factors, strong cross-functional
teams, stronger commitments to these teams, design for quality, and design for
manufacturability techniques have been identified. Necessary elements were cited
to be joint working with suppliers and customers on production planning,
inventory management, replenishment, forecasting, and demand planning.225

Barratt has identified yet another, however closely related, set of elements that
define collaboration. These are (1) cross-functional activities, (2) process
alignment, (3) joint decision making, and (4) supply chain metrics. The elements
that support a collaborative culture are trust, mutuality, information exchange,
openness, and communication, which in turn is necessary for successful
collaboration.226 It is important to note that a rather close proximity to team
working exists. As Christopher remarked: “The closer the relationship between
buyer and supplier the more likely it is that the expertise of both parties can be
applied to mutual benefit.”227 Consequently, higher levels of internal and external
collaboration are expected to improve performances in the areas of
collaboration.228 As such, these capabilities also underlie previous management
concepts, such as Total Quality Management (TQM) and, very closely related,

223
Liedtka, Jeanne M.: Collaborating across lines of business for competitive advantage,

in: Academy of Management Executive, Vol. 10 (1996), No. 2, p. 21.
224 Cf. Liedtka: Collaborating across lines of business for competitive advantage,

pp. 21-25.
225 Cf. Deloitte & Touche LLP, Koudal, Peter: Mastering complexity in global

manufacturing: Powering profits and growth through value chain synchronization 2003,

pp. 20-23.
226 Cf. Barratt, Mark: Understanding the meaning of collaboration in the supply chain, in:

Supply Chain Management: An International Journal, Vol. 9 (2004), No. 1,

pp. 35-37.
227 Christopher: Logistics and supply chain management: Creating value-adding networks,

p. 201.
228
Cf. Stank, Keller and Daugherty: Supply chain collaboration and logistical service
performance, pp. 32-33.


Improving Supply Chain Performance

Collaborative Planning, Forecasting and Replenishment (CPFR). In fact, with the
emergence of CPFR in the mid-1990s, collaboration has become more recognized
in the context of SCM.229

Spekman, Kamauff Jr., and Myhr have drawn a similar conclusion. In their
view, cooperation refers to rudimentary information exchange with little
interaction and is seen as a necessary but not sufficient condition for managing
business relationships. The next level would then be coordination. JIT and EDI
linkages can reflect such coordinated relationships. Again, though companies
cooperate and coordinate, they still might not behave as true partners. In order to
achieve collaboration, a level of trust and commitment beyond that found in
cooperation and coordination is required. Thus, supply chain partners may
cooperate and coordinate, but still not collaborate.230

In the context of SCM, trust has been defined as “[…] one’s belief that one’s
supply chain partner will act in a consistent manner and do what he/she says
he/she will do.”231 This definition neglects the beneficial nature of trust-based
relationships. Robbins has provided a general definition of trust: “Trust is a
positive expectation that another will not – through words, actions, or decisions –
act opportunistically.”232 As characteristics of a trust-based relationship, Robbins
has identified integrity, competence, consistency, loyalty, and openness.233 Based
on a cross-discipline analysis, Rousseau et al. have found that a common
understanding of trust among scholars exists. This understaning has been
summarized in the following definition: “Trust is a psychological state comprising
the intention to accept vulnerability based upon positive expectations of the
intentions or behavior of another.”234 In contrast to trust, commitment refers to the
belief that companies are dedicated and willing to invest resources to guard a
relationship.235

Collaboration mainly materializes on the process level. In this light, it can
relate to specific processes, such as procurement, demand, inventory, capacity,

229 Cf. Barratt: Understanding the meaning of collaboration in the supply chain,

pp. 30-39.
230 Cf. Spekman, Kamauff Jr. and Myhr: An empirical investigation into supply chain

management: A perspective on partnerships, pp. 55-56.
231 Cf. Spekman, Kamauff Jr. and Myhr: An empirical investigation into supply chain

management: A perspective on partnerships, p. 56.
232 Robbins, Stephen P.: Organizational behavior, 11th ed., Upper Saddle River 2005,

p. 356.
233 Cf. Robbins: Organizational behavior, p. 356.
234 Rousseau, Denise M. et al.: Not so different after all: A cross-discipline view of trust,
in: The Academy of Management Review, Vol. 23 (1998), No. 3, July, p. 395.
235 Cf. Spekman, Kamauff Jr. and Myhr: An empirical investigation into supply chain
management: A perspective on partnerships, pp. 55-57.


Cooperation as Success Factor for Supply Chain Management

and product development or to general collaboration terms. In the latter case,
general collaborative rules and standards are established between supply chain
partners.236 The complementary nature of collaboration and SCM becomes evident
when reviewing success factors for collaboration and collaborative relationships.
Hammer, for instance, has emphasized process orientation, distributed decision
making, and collaborative style as requirements for successful cross-company
collaboration.237 Christopher has pointed out the importance of processes as a
series of interactions between the parties involved and generally joint objectives
among supply chain partners.238 Bowersox, Closs, and Stank have mentioned three
factors of enhanced collaboration: (1) mutual trust and shared visions and
objectives, (2) clear structures based on rules, agreements, and guidelines to
encourage risk and benefit sharing, and (3) pre-agreement on exit procedures in
case of adversarial behavior of partners or other reasons for ending the
collaboration. This view, however, leaves out some important issues seen as
critical by other authors, such as information sharing.239

A study conducted by Meritus, IBM, and CSR back in 1995 has identified the
use of IT for relationship-specific processes, major capital and resource
commitments on each side of the relationship, shared information such as costs,
point-of-sales data and future plans, and shared efficiencies for enhancing end
customer value as characteristics of advanced relationships.240 This entire
understanding of collaboration matches well the SCM framework developed in
this text. These arguments help justify the positioning of the notion of
collaboration at the core of the SCM framework.

Practice leaders report benefits such as inventory reductions, lower operating
costs, and potentially profit gains through coordination and collaboration.241 Basch
has stated that collaboration with channel partners is the most effective strategy

236 See Kilger, Christoph and Boris Reuter: Collaborative planning, in: Stadtler, Hartmut

and Christoph Kilger (Eds.): Supply chain management and advanced planning:

Concepts, models, software and case studies (2nd ed.), Berlin Heidelberg New York

2002, pp. 226-231.
237 Cf. Hammer: The superefficient company, p. 90.
238 Cf. Christopher: Logistics and supply chain management: Creating value-adding

networks, p. 495.
239 Cf. Bowersox, Donald J., David J. Closs and Theodore P. Stank: Ten mega-trends that

will revolutionize supply chain logistics, in: Journal of Business Logistics, Vol. 21

(2000), No. 2, p. 5.240 Cf. Neuman, John and Samuels Christopher: Supply chain integration: vision or reality?

in: Supply Chain Management: An International Journal, Vol. 1 (1996), No. 2, pp. 7-10.
241 Cf. Poirer, Charles C.: Achieving supply chain connectivity, in: Supply Chain

Management Review (2002), November/December, p. 18.


Improving Supply Chain Performance

for manufacturers.242 Still, many companies are unwilling or unable to share
sensitive data that could be beneficial for both parties. They protect information in
order to sustain a advantageous position.243 This behavior can be interpreted as a
lack of trust. Therefore, trust is considered to be the most critical element of
collaboration. It can be a great enabler but also a powerful barrier for
collaboration.244

The last ingredient of the core SCM model, indeed that component of SCM
cooperation, which supplements coordination and collaboration, is integration.
Many authors writing about integration seem to enhance its meaning beyond the
one intended in the SCM framework developed in this text. This might be due to
linguistic reasons, but it is important to clarify those differences.

Hertz, for example, has developed a broad understanding of integration and
has defined it as “a process of coordinating activities, resources, and organizations
in order to function in concert.”245 Similarly, Kahn and Mentzer have seen
integration as bringing parts together into a cohesive organization.246 Two
elements have been identified that bring about integration: interaction and
collaboration. Both elements were introduced as separate philosophies and
combined as integration. The interaction philosophy emphasizes exchange of
information through meetings, phone calls and similar communications. The
collaboration philosophy is seen similar to the relationship marketing philosophy
in the marketing discipline. Emphasis is laid on strategic alignment through a
shared vision, collective goals, and joint rewards, along with an informal structure
of managing relationships. It is considered to be an attitudinal approach that does
not focus on establishing information linkages, but rather on building an esprit de
corps. Combined, integration then is viewed as comprising interaction and
collaboration activities.247 Though Kahn and Mentzer have applied their
interpretation to an interdepartmental setting, this understanding has also been
transferred by others to inter-company relationships.248

242 Cf. Basch, Michael D.: Harness the power of the Internet: A new model for the 21st

century., in: Information Executive, Vol. 4 (2000), No. 10, October, pp. 8-9.
243 Cf. Poirer: Achieving supply chain connectivity, p. 18.
244 See also section B.IV. for an extended discussion of supply chain relationships.
245 Hertz: Dynamics of alliances in highly integrated supply chain networks, p. 239.
246 Cf. Kahn, Kenneth B. and John T. Mentzer: Logistics and interdepartmental integration,

in: International Journal of Physical Distribution & Logistics Management, Vol. 26

(1996), No. 8, p. 9.247 Cf. Kahn and Mentzer: Logistics and interdepartmental integration, pp. 7-9.
248 For another interdepartmental usage, cf. Stank, Daugherty and Ellinger:

Marketing/logistics integration and firm performance, p. 12. Authors, who expanded

this understanding to inter-company relationships are Stank, Keller and Daugherty:

Supply chain collaboration and logistical service performance, p. 31; and also Lee, Hau


Cooperation as Success Factor for Supply Chain Management

Integration is perceived differently in this text. Coordination and collaboration
includes the interaction and collaboration notions described by Kahn and Mentzer
as part of their understanding of integration. In contrast, integration should be
considered separately with a distinct meaning. This is also more in line with the
following definition of the act of integrating: “To make into a whole by bringing
all parts together; unify.”249 According to this, unification of once separate parts is
implied. In the overall SCM context, this may only be desired in some areas, in
particular in the material and information flows along supply chain processes.
Diversity in contrast to homogeneity may be beneficial especially in collaborative
efforts, as defined above.250 Therefore, integration refers mainly to a seamless
material and information flow of all members within a supply chain with the
objective to maximize competitive advantage.251

Schmenner and Swink have referred to this in the context of manufacturing
operations as the Theory of Swift, Even Flow. According to this theory, “[…] the
more swift and even the flow of materials through a process, the more productive
that process is. Thus, productivity for any process “[…] rises with the speed by
which materials flow through the process, and it falls with increases in the
variability associated with the flow, be that variability associated with the demand
on the process or with steps in the process itself.”252 Though material flows are of
relevance,253 the information flow is not only of crucial importance for
coordination and collaboration, but is also seen as key to a seamless supply
chain.254

In the context of integration, however, the emphasis is not on what, how, or in
what kind of relationship information should be shared. Rather, integration aims at
facilitating the agreed upon way of coordination and collaboration in the most
effective way. Though operational aspects can be derived directly from it, the

L.: Creating value through supply chain integration, in: Supply Chain Management

Review (2000), September/ October, pp. 32-36.

249
The American Heritage Dictionary of the English Language.

250
Cf. also section B.I.3.a. for the context in the SCM framework. Especially teams and
team-like relationships rely on bringing diverse parts together in order to create new
knowledge, cf. Robbins: Organizational behavior, pp. 281-283.

251
Cf. Mason-Jones, R. and D. R. Towill: Information enrichment: designing the supply
chain for competitive advantage, in: Supply Chain Management: An International
Journal, Vol. 2 (1997), No. 4, p. 137; and Frohlich, Markham T. and Roy Westbrook:
Arcs of integration: An international study of supply chain strategies, in: Journal of
Operations Management, Vol. 19 (2001), p. 186.

252
Schmenner and Swink: On theory in operations management, p. 102.

253
With regard to material flow and logistics integration, higher efficiency and
productivity has been observed, for example see Stank, Keller and Daugherty: Supply
chain collaboration and logistical service performance, p. 31.

254
Cf. Towill: The seamless supply chain - the predator's strategic advantage, pp. 52-53.


Improving Supply Chain Performance

strategic dimension is of relevance as part of SCM cooperation. Strategic
relevance for integration is derived by creating better information, which in turn
fuels coordination and collaboration.255 Therefore, not only does the management
of key business processes across the supply chain determine success, but so also
their integration.256

Balancing supply and demand becomes easier the more integrated information
flows between customers and suppliers are. Before the diffusion of the Internet
and before common IT infrastructures became available, such high integration was
infeasible and was achieved only by using regular mail, telephone, and fax,
followed by early forms of EDI.257 Therefore, integration gained in importance
through the proliferation of IT and the Internet because real-time information
sharing has become feasible.258 Hertz has noted that the growing connectedness
requires a higher degree of standardization, formalization, homogenization,
communication, and simplicity of companies in supply chains.259 Therefore, it is
no surprise that Schönsleben has defined integration as “the ability of a
comprehensive information system to exchange information.”260 Consequently,
section B.III.3. discusses the importance of standardization, IT systems, and e-
business for integration in greater detail.

The synergistic nature of coordination, collaboration, and integration is evident
in several focused concepts that have recently been promoted and successfully
applied. For example, design for logistics (DFL) as a variation of design for
manufacturing aims at designing products and packages in a way that minimizes
transportation and storage costs.261 Tracking and tracing of product orders is
another development that can be directly linked to the simultaneous and balanced
application of the elements of the core SCM model. More industry-specific
packages such as ECR and CPFR, as previously mentioned, provide
comprehensive guidelines to implement the SCM idea in the retailing industry

255
See Lewis, Ira and Alexander Talalayevsky: Logistics and information technology: A
coordination perspective, in: Journal of Business Logistics, Vol. 18 (1997), No. 1,

p. 145. About the importance of information, see next section B.II.3.c.
256 See Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, p. 15.
257 Cf. Frohlich, Markham T.: E-integration in the supply chain: Barriers and performance,
in: Decision Sciences, Vol. 33 (2002), No. 4, p. 538.
258 Cf. Frohlich and Westbrook: Demand chain management in manufacturing and
services: Web-based integration, drivers and performance, p. 731.
259 Cf. Hertz: Dynamics of alliances in highly integrated supply chain networks, p. 240.
260 Schönsleben: Integrales Logistikmanagement: Planung und Steuerung der umfassenden

Supply Chain, p. 425.

261
Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply
chain: Concepts, strategies, and case studies, pp. 215-216. DFL is especially relevant
for the material flow.


Cooperation as Success Factor for Supply Chain Management

environment.262 The idea of fourth party logistics service providers (4PL) was
originated by the consulting firm Accenture and extends the functions of present
logistics service providers to include planning, management, and process control
of entire supply chains.263

3.
Importance of Information for Coordination, Collaboration and Integration
in Supply Chains
Information is of crucial importance in SCM cooperation because it is present in
all three elements of the core SCM model. It can be seen as the “glue” that holds
together business structures, processes, and entire supply chains.264 Some even see
information as an independent production factor, in addition to the traditional
production factors of material, capital, and human capital.265 In general, a
distinction can be drawn between the volume of information and the richness of
information exchanged. In the case of coordination, the amount of information
exchanged is generally larger, whereas the information exchanged in collaborative
relationships is richer. Evans and Wurster have differentiated between the reach of
information and the richness of information. Reach refers to the number of people
or companies exchanging information and therefore to connectivity. Richness is
characterized by the dimensions bandwidth, customization, and interactivity.
Bandwidth refers to the amount of information that can be moved between sender
and receiver. Customization differentiates between mass customization and
individual conversation. Interactivity determines whether a monologue or a
dialogue type of information exchange is conducted.266

When information is defined, the most common reference is the one made to
the data, information, knowledge, and wisdom (DIKW) hierarchy.267 According to

262 For example, cf. Werner: Supply Chain Management: Grundlagen, Strategien,
Instrumente und Controlling, pp. 120-124.
263 Cf. Christopher: Logistics and supply chain management: Creating value-adding
networks, pp. 295-297.

264
Cf. Sanders, Nada R. and Robert Premus: IT applications in supply chain organizations:
A link between competitive priorities and organizational benefits, in: Journal of
Business Logistics, Vol. 23 (2002), No. 1, p. 65.

265
See Fulkerson: Information-based manufacturing in the informational age,
pp. 131-132. For traditional production factors, see for example Wöhe, Günter and
Ulrich Döring: Einführung in die allgemeine Betriebswirtschaftslehre, 22nd ed.,
München 2005, p. 46.

266
Cf. Evans, Philip B. and Thomas S. Wurster: Strategy and the new economics of
information, in: Harvard Business Review, Vol. 75 (1997), September/October, p. 73.

267
Tracing back the origins of the DIKW hierarchy, Sharma concludes that though the
DIKW hierarchy is used in many research fields, these references fail to trace it back to
its origins. As the ultimate source, Sharma identified Cleveland, Harland: Information


Improving Supply Chain Performance

the DIKW hierarchy, the lowest level of content is represented by data.
Information is produced by putting data in context. As Duè remarked: “Data, by
itself, has little value. Data must be turned into information by being organized,
modeled, formatted, edited, verified, placed in context, and delivered in a timely
manner to decision makers before it takes on value.”268 Davenport and Prusak
have suggested the “five Cs” as methods to transform data into information.
According to this, data has to be (1) contextualized, (2) categorized, (3) calculated,

(4) corrected, and/or (5) condensed in order to become information.269
Knowledge represents the next level in the DIKW hierarchy. In order to aquire
knowledge, information has to be transformed or applied for a purpose.
Knowledge can be seen as a set of justified true beliefs.270 Nonaka has pointed out
the importance of information in the knowledge creation process: “Information is
a necessary medium or material for initiating and formalizing knowledge […].”271
A distinction can be drawn between explicit knowledge and tacit knowledge.
Explicit knowledge can be communicated in formal, systematic language whereas
tacit knowledge is more personal and is deeply rooted in action, commitment, and
involvement in a specific context. Tacit knowledge is thus much harder to share
with others. This sharing with others is of crucial importance in order to make
knowledge accessible for organizations. Though knowledge cannot be created
without individuals, their ability to create new knowledge can be supported and
enhanced by organizations.272 Organizational learning theory is based on this
realization and suggests that the result of organizational learning is more than the
sum of each member’s knowledge. In addition, it is believed that organizations
can learn independently from their members and store this knowledge in

as resource, in: The Futurist (1982), December, pp. 34-39, who makes reference to a
1934 poem by T.S. Eliot that was then expanded by Cleveland to form the DIKW
hierarchy. Expansions of the hierarchy were for example the addition of the level
understanding between knowledge and wisdom by Ackoff, R. L.: From data to wisdom,
in: Journal of Applied Systems Analysis, Vol. 16 (1989), pp. 3-9 or the addition of
enlightenment as sort of a meta-learning level on top of wisdom by Zeleny, M.:
Management support systems: Towards integrated knowledge management, in: Human
Systems Management, Vol. 7 (1987), No. 1. See Sharma, Nikhil: The origin of the
DIKW hierarchy, 2005, http://www-personal.si.umich.edu/~nsharma/dikw_origin.htm,
retrieved on: February 15, 2006 for an overview.

268 Cf. Duè, Richard T.: The value of information, in: Information Systems Management,
Vol. 13 (1996), No. 1, Winter, p. 68.

269
Cf. Davenport, Thomas H. and Laurence Prusak: Working knowledge: How
organizations manage what they know, Boston, MA 1998, p. 4.

270
Cf. Nonaka, Ikujiro: A dynamic theory of organizational knowledge creation, in:
Organization Science, Vol. 5 (1994), No. 1, February, p. 15.

271
Nonaka: A dynamic theory of organizational knowledge creation, p. 16.

272
Cf. Nonaka: A dynamic theory of organizational knowledge creation, pp. 16-17.


Cooperation as Success Factor for Supply Chain Management

nonhuman repositories that are evident as strategies, structures, systems, culture,
and routines.273

On top of knowledge, wisdom refers to the understanding of the underlying
principles and theories that explain why the applied knowledge works and
completes the DIKW hierarchy. This classic DIKW hierarchy has been altered and
expanded by several authors, but the basic principle prevails. The boundaries
between the levels are rather vague and the point at which data turns into
information can be considered to be a philosophical one.274

Information and its use is potentially the most important determinant of
successful SCM as it directly influences all aspects of the SCM framework.
Moberg et al. have found in their research and literature review that information
flow facility structure was the only component to be identified on virtually every
occasion of common SCM components.275 Though information has always been a
key aspect of management, developments in information processing and
exploration technology increased the importance of information management for
SCM.276 As illustrated before, integrated and coordinated decisions in supply
chain networks require a free flow of relevant information.277

Acknowledging the importance of information for SCM raises the question of
how important it is. Many researchers have tried to capture the value of
information by different methods. In order to determine the value of information,
Li et al. have examined twelve representative models. Based on their comparative
analysis they conclude that information sharing has value for SCM, but also that it
may not be the only way to achieve optimal performance. In general, suppliers
gain higher profits than retailers by sharing information. In terms of relevant
factors influencing the value of information sharing, they conclude that it is highly
dependent on the specific supply chain situation.278 Cachon and Fisher and

273
See Sabherwal, Rajiv and Sanjiv Sabherwal: Knowledge management using
information technology: Determinants of short-term impact on firm value, in: Decision
Sciences, Vol. 36 (2005), No. 4, December, pp. 533-536. For one of the first
comprehensive books on the learning organization, see Senge, Peter M.: The fifth
discipline, New York 1990.

274
Cf. Davenport: Process innovation, p. 71.

275
Cf. Moberg, Christopher R. et al.: Do the management components of supply chain
management affect logistics performance? in: The International Journal of Logistics
Management, Vol. 15 (2004), No. 2, p. 17.

276
Cf. Johnson, M. Eric and Seungjin Whang: E-business and supply chain management:
An overview and framework, in: Production and Operations Management, Vol. 11
(2002), No. 4, p. 413.

277 Cf. Vakharia: E-business and supply chain management, p. 497.
278 Cf. Li, Gang et al.: Comparative analysis on value of information sharing in supply
chains, in: Supply Chain Management: An International Journal, Vol. 10 (2005), No. 1,


Improving Supply Chain Performance

Robinson, Sahin, and Gao have also provided an extensive literature review of a
variety of models that investigate the impact of information sharing on
performance in different settings. Again, depending on the specific settings,
benefits vary, but in almost all models, information sharing improves supply chain
cost performance directly or indirectly between 0% and 35%.279

Cachon and Fisher have also developed their own, distinct model. Their
finding is that a quicker and more even flow of goods through the supply chain is
more beneficial than information sharing.280 Achieving a quicker and more even
flow of goods requires at least improved information processing capabilities and
therefore information sharing also influences that indirectly. It is also
acknowledged that in an environment with higher demand uncertainty, the value
of information sharing may increase.281 Despite the proven impact of information
sharing, Lee and Whang have pointed out that information sharing is only an
enabler for better coordination and planning of the supply chain. Accordingly,
companies must develop capabilities to make use of information.282

As for what information should be shared, it is clear that processes that span
several companies can only be optimized if all information relevant to these
processes is available to all companies involved. Typically, the following types of
information are of relevance:283

-
Inventory level. This includes all kinds of inventory, such as material,
work in progress, finished goods, and goods in transit.

pp. 42-44. This conclusion also underscores the importance of a contingency approach,
as proposed on the normative level of the SCM framework.

279
Cf. Cachon, Gérard P. and Marshall Fisher: Supply chain inventory management and
the value of shared information, in: Management Science, Vol. 46 (2000), No. 8,

p. 1034; and Robinson Jr., E. Powell, Funda Sahin and Li-Lian Gao: The impact of e-
replenishment strategy on make-to-order supply chain performance, in: Decision
Sciences, Vol. 36 (2005), No. 2, p. 37.
280
Cf. Cachon and Fisher: Supply chain inventory management and the value of shared
information, p. 1046. This supports the Theory of Swift, Even Flow postulated by
Schmenner and Swink: On theory in operations management, pp. 102-103.

281
See Cachon and Fisher: Supply chain inventory management and the value of shared
information, p. 1046.

282
Cf. Lee and Whang: Information sharing in a supply chain, p. 386.

283
According to Lee and Whang, cf. Lee and Whang: Information sharing in a supply

chain, pp. 375-381.


Cooperation as Success Factor for Supply Chain Management

-
Sales data. Ultimate sales data lessen the negative effects of distorted
demand information, as shown for example in the beer game284, when
simulated with visible end consumer demand.

-
Sales forecast. Since companies adapt their plans to their forecasts, it is
important to share these expectations. If sales data are shared, every
company in the supply chain could do their forecasts based on ultimate
sales data. However, different methods might lead to differing results.

-
Order status for tracking and tracing. This supports mainly customer
service and reduces uncertainty in the supply chain and for the ultimate
customer.

-
Production and delivery schedules. The different tiers in a supply chain
can align their operations to support the whole process if production and
delivery schedules are shared, as is the case for just-in-time relationships.

-
Capacity. Sharing capacity information, especially production and
transportation capacities, can mitigate shortage and gaming behavior and
supports supply chain planning.

-
Performance metrics. This includes all performance metrics that are
relevant for the whole process under consideration. Examples are quality
data, lead times, queuing delays, and service performance, to name a few.

In addition to the points listed, cost accounting figures are also of high
relevance. Information about selling price, salvage value, variable production cost,
and fixed production cost, for example, are important to complete the
informational foundation necessary for optimal decisions. However, this kind of
information is highly sensitive and reservations about sharing it do exist. The
benefits of such shared information are undisputed and all information mentioned
before could be used in highly integrated and aligned organizations for better
decisions. Nevertheless, there are obstacles that prevent companies from sharing
such information. This is mainly based on the prevailing belief that information
represents power and sharing it would lead to a loss of power and threaten the
sharer’s position in the supply chain. Traditionally, relevant information has been
a substantial source of strategic advantage, which is in line with economic theory,
where a monopolistic or monopsonistic position promises to retain all profits.285
Profits associated with superior information are often referred to as informational

284 See Sterman: Modeling managerial behavior: Misperceptions of feedback in a dynamic
decision making experiment, pp. 321-339.
285 Cf. Kahl and Berquist: A primer on the internet supply chain, p. 48; and Lee and
Whang: Information sharing in a supply chain, p. 385.


Improving Supply Chain Performance

rent.286 In such a constellation, however, available and retrievable information can
only be exploited, but not properly leveraged.287 This is a major challenge for
supply chains and is therefore discussed in greater detail as part of section B.IV.

Another aspect of information sharing is the quality of shared information.
Quality in general has many dimensions and its meaning depends highly on the
context. One widely accepted definition of quality is provided by the International
Organization of Standardization (ISO). They define quality as the degree to which
a set of inherent features of a product or service fulfills customer requirements.288
In the context of SCM, quality can be interpreted as the fulfillment of customer
requirements in terms of physical-functional specifications of products or in terms
of an expected outcome of processes.289 Quality of information in supply chains
can be interpreted similarly. In contrast to the customer orientation of entire
supply chains, all supply chain members who rely on information are addressees,
and therefore customers, of information. Therefore, quality of information must be
defined according to how the information is perceived and used by each supply
chain member separately.290 Miller has presented ten dimensions of information
quality that characterize the overall quality of information:291

-
Relevance. The information addressee’s needs define the relevance of
information. This does not mean that irrelevant information is of poor
quality per se, but in the wrong context, it might be irrelevant.

-
Accuracy. Information should reflect the underlying reality. Problems
may arise when information becomes too accurate for its purpose and lead
to an information overload.

-
Timeliness. In contrast, information can rarely be too timely. Stalk and
Hout note that as information ages, it loses value. With time as an

286
Cf. Lee and Whang: Information sharing in a supply chain, p. 385. For a game
theoretical consideration of informational rents, see Fudenberg, Drew and Jean Tirole:
Understanding rent dissipation: On the use of game theory in industrial organization, in:
The American Economic Review, Vol. 77 (1987), No. 2, May, pp. 179-182.

287 Cf. Bowersox, Closs and Stank: Ten mega-trends that will revolutionize supply chain
logistics, p. 10.
288 Cf. n.a.: International Organization of Standardization, section ISO 9000, introduction,
understanding the basics.

289 See Heringer, Crispin: Qualitätsmanagement, in: Wannenwetsch, Helmut H. (Ed.):
Vernetztes Supply Chain Management, Berlin Heidelberg New York 2005, p. 363.
290 Cf. Miller, Holmes: The multiple dimensions of information quality, in: Information


Systems Management, Vol. 13 (1996), No. 2, Spring, p. 79.
291 Cf. Miller: The multiple dimensions of information quality, pp. 79-81.


Cooperation as Success Factor for Supply Chain Management

increasingly important competitive factor, the importance of fresh and upto-
date information increases too.292

-
Completeness. Completeness of information has to be seen in light of its
context.

-
Coherence. Though a separate dimension, it heavily relies on accuracy
and/or timeliness. When information is incoherent, it usually is inaccurate
and/or already too old.

-
Format. The underlying form refers to the way information is presented.

-
Accessibility. With increasing accessibility, the quality of information
increases as well. Information that can not be obtained when needed is of
very limited value. Accessibility is strongly associated with timeliness of
information.

-
Compatibility. This refers to how well information can be processed with
tools and combined with other information.

-
Security. Security can be divided into logical security, which refers to
fraud protection, and disaster recovery, which refers to natural disasters
and facility failure.

-
Validity. Information is valid when its truth can be verified and it satisfies
appropriate standards related to the other dimensions.

Gosain, Malhotra, and El Sawy have confirmed the importance of the above
mentioned dimensions and point out that quality of information even gains in
importance because manual filtering might disappear more and more. Although
automated information processing prevents manual mistakes, it also makes the
process less transparent and therefore, wrong information or information of low
value might be generated if the information input is already of bad quality and not
properly checked.293

292
Cf. Stalk and Hout: Competing against time, p. 238.

293
See Gosain, Sanjay, Arvind Malhotra and Omar A. El Sawy: Coordinating for
flexibility in e-business supply chains, in: Journal of Management Information Systems,
Vol. 21 (2004), No. 3, Winter, pp. 31-32.


Improving Supply Chain Performance

III. Holistic View of Supply Chain Management
1. Role of Strategic Fit in Supply Chains
In the context of supply chains, Fisher’s contribution has often been cited as the
foundation of strategic fit between product characteristics and supply chain
characteristics.294 The importance of matching the two dimensions is widely
accepted. Essentially, Fisher’s model states that in cases of uncertain demand, a
supply chain should be designed in a responsive manner whereas stable demand
indicates a more efficient approach.295 This is translated into the distinction
between innovative products for an uncertain environment characterized by
shorter product life-cycles and functional products for a more stable environment
with longer product life cycles. The underlying dimensions are unpredictable,
uncertain demand versus stable, predictable demand.296 The underlying concept of
strategic fit can be traced back to Skinner’s contribution on manufacturing and
strategy.297

With regard to the manufacturing function, Skinner has made important
remarks on several strategic shortcomings that can be applied not only to the
manufacturing function but also to the current discussion on SCM. For a better
understanding, it is useful to review Skinner’s work more explicitly and more
thoroughly.

One of Skinner’s key observations has been the shortsighted view of managers
to confuse productivity with competitiveness. The criticism was that
manufacturing was expected to be efficient without specifying what is actually
meant by efficiency. A tendency to define efficiency as low costs can cause the
manufacturing functions to be misaligned with the overall strategic position.298
The same can be said for SCM. Many still consider SCM to be an operational
logistics concept, disregarding the greater significance of “real”, holistic SCM.299
Efficiency has to be seen relative to the strategic position, in line with Skinner’s
notion. Skinner has identified the following factors that lead to such misaligned

294 Cf. Fisher, Marshall L.: What is the right supply chain for your product? in: Harvard

Business Review, Vol. 75 (1997), March-April, pp. 105-116.
295 Cf. Fisher: What is the right supply chain for your product?, p. 109.
296 Cf. Fisher: What is the right supply chain for your product?, pp. 106-109. It should be

noted that long product life-cycles are not necessarily more stable, but they tend to.
297 Cf. Hayes and Pisano: Beyond world-class: The new manufacturing strategy, p. 80; and

Skinner, Wickham: Manufacturing - missing link in corporate strategy, in: Harvard

Business Review, Vol. 47 (1969), May/June, pp. 136-137.
298 Cf. Skinner: Manufacturing - missing link in corporate strategy, pp. 136-140.
299 What is referred to as “real” SCM is the SCM framework identified in this text. It

especially refers to the potential the holistic approach holds.


Holistic View of Supply Chain Management

behavior: (1) personal inadequacy of managers and personnel in the function, and

(2) lack of awareness of trade-offs and compromises.300
The first factor is especially relevant for SCM because the complexity to cope
with in SCM is orders of magnitude larger than that of individual functions. The
necessity of trade-offs is in line with subsequent work that has taken up this issue
and developed it further, such as done by Porter.301 Skinner has used an illustrative
example to point this law of trade-offs out. Even almost forty years after Skinner
has made this comparison, it is not possible to land a 500 passenger airplane on a
carrier and break the sonic barrier, though each task in itself has been achieved by
now.302 There is no viable reason for which this law of trade-offs should not hold
true for SCM.303 Simchi-Levi, Kaminsky, and Simchi-Levi have identified five
exemplary trade-offs: (1) lot-size vs. inventory, (2) inventory vs. transportation
costs, (3) lead-time vs. transportation costs, (4) product variety vs. inventory, and

(5) cost vs. customer service.304 It follows that if a product or service is to compete
on a certain set of characteristics, the supply chain is to be aligned so that it
supports this set of characteristics in the best way achievable.305 Effectiveness then
relates to the achieved performance that is relevant for sustainable success, i.e.
market position, and efficiency to operational effectiveness.306
Recognizing Porter’s view of strategic fit and the existence of trade-offs,
Chopra and Van Mieghem remarked: “The goal is to create a fit between the
desired strategic position and the supply chain capabilities and processes used to
satisfy customer needs and priorities.”307 The decision-making process follows a
hierarchy that originates from strategic choices subject to capabilities at hand. In
this sense, the possibilities suggested by the SCM framework extend those
capabilities and provide a variety of choices for positioning a strategy. As generic
determinants for the supply chain strategy, Fisher’s dimensions may build the
foundation.308 Others have adopted and adjusted it later on. Chopra and Meindl,

300
Cf. Skinner: Manufacturing - missing link in corporate strategy, p. 138.

301
Cf. Porter: What is strategy?, pp. 68-70.

302
This remark refers to an example provided by Skinner, see Skinner: Manufacturing -
missing link in corporate strategy, p. 140.

303
Cf. Schmenner and Swink: On theory in operations management, pp. 106-107 for the
notion on the law of trade-offs.

304
Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply
chain: Concepts, strategies, and case studies, pp. 113-116.

305
The set of characteristics for competition consists of those perceived relevant by the
customer, such as product-specific characteristics, price, distribution channels, and
advertising, to name a few.

306
Cf. Porter: What is strategy?, pp. 61-64.

307
Chopra and Van Mieghem: Which e-business is right for your supply chain?, pp. 32-33.
For a more detailed discussion of strategic fit, see Porter: What is strategy?, pp. 70-75.

308
Cf. Fisher: What is the right supply chain for your product?, pp. 107-108.


Improving Supply Chain Performance

for example, have simply replaced functional and innovative products all together
with the underlying demand uncertainty. The greater the demand uncertainty the
more responsive the supply chain should be.309 Based on this, Chopra and Meindl
have noted that there indeed exists a right supply chain strategy for a given
corporate strategy.310 Moreover, they see a leverage of this idea by extending it to
supply chain fit. Misalignments between companies are even more likely as
organizations differ in their cultures. Therefore, achieving inter-company and
inter-functional strategic fit is seen to lead to an overall more competitive supply
chain.311 Furthermore, as complexity increases drastically compared to a
company-internal scope, such a constellation is much more sustainable as it is
even harder to imitate.312

Instead of using the terms efficient supply chain and responsive supply chain,
the terms agile and lean are preferred by several authors. In cases of long lead
times and predictable demand, a lean supply chain setting is suggested by
Christopher. In case of short lead times and unpredictable demand, an agile supply
chain is indicated. In contrast to Fisher, Christopher considers the combination of
unpredictable demand and long lead times as well as predictable demand with
short lead times not necessarily as a mismatch. For an unpredictable demand with
long lead times in the supply chain, a hybrid strategy is suggested that makes use
of postponement. For a predictable demand environment with short lead times in
the supply chain, a continuous replenishment strategy is suggested. These strategy
suggestions make sense when there is little chance to influence lead times.313

Another variation is provided by Simchi-Levi, Kaminsky, and Simchi-Levi.
Their generic supply chain strategies also share the notion of demand uncertainty.
In supply chain characteristics, they have differentiated between pull systems and
push systems.314 Push systems rely on forecasts – that is why they are also
sometimes called speculative processes315 – and make use of economies of scale in
order to achieve a cost efficient production or fulfillment. Therefore, they are
related to an efficient supply chain. Pull systems, on the other hand, rely on short

309 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 38.
310 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,
p. 40.
311 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,
pp. 46-48.
312 Cf. Porter: What is strategy?, pp. 73-75.
313 Cf. Christopher: Logistics and supply chain management: Creating value-adding

networks, pp. 117-119.
314 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply
chain: Concepts, strategies, and case studies, pp. 121-125.
315 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation, p

8. and p. 14.

Holistic View of Supply Chain Management

lead times and hold little inventory. Therefore, they cannot make use of economies
of scale and the planning horizon is relatively short. This is why pull systems are
also called reactive systems.316 In case of high demand uncertainty and low
economies of scale, a pure pull strategy is indicated, which is similar to
Christopher’s agile supply chain. In case of low demand uncertainty and high
economies of scale, a pure push-strategy is suggested, which relates to
Christopher’s lean supply chain.

Simchi-Levi, Kaminsky, and Simchi-Levi have been more cautious with their
indications. In case of low demand uncertainty and low economies of scale, they
suggest a push-pull strategy, requiring a decoupling point. The decoupling point
can be seen as the point where the customer-facing part of a supply chain is
separated from that part of the supply chain that is based on planning.317
Therefore, inventory often buffers the fluctuating demand in order to smooth
operations. Christopher has referred to this buffer inventory as “strategic
inventory”.318 Furthermore, the two distinct supply chain parts coordinate at this
point and exchange demand forecasts. The historical data used for a forecast is
provided by the pull section and determines the supply chain planning process and
buffer inventory.319 If high demand uncertainty and high economies of scale are
indicated, they have suggested a pull-push strategy, essentially turning the push-
pull strategy around. Again, a decoupling point between these two different types
of supply chains is required.320

The concept of postponement aims to increase the portion of the supply chain
that operates in a pull mode.321 The lean paradigm can be applied upstream from
the decoupling point in the supply chain and downstream from the decoupling
point, the agile paradigm is indicated. Consequently, the position of the
decoupling point depends upon the longest lead time customers are willing to
accept.322

316 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 8 and p. 14.
317 Cf. Naylor, Ben J., Mohamed M. Naim and Danny Berry: Leagility: Integrating the lean

and agile manufacturing paradigms in the total supply chain, in: International Journal of

Production Economics, Vol. 62 (1999), p. 112.
318 Cf. Christopher: Logistics and supply chain management: Creating value-adding

networks, pp. 119-121.
319 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply

chain: Concepts, strategies, and case studies, pp. 126-127.
320 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply

chain: Concepts, strategies, and case studies, pp. 124-125.
321 Cf. Chopra and Van Mieghem: Which e-business is right for your supply chain?, p. 35.
322 Cf. Naylor, Naim and Berry: Leagility: Integrating the lean and agile manufacturing

paradigms in the total supply chain, pp. 112-115.


Improving Supply Chain Performance

Although both paradigms are fundamentally different, efficient and responsive
supply chains still share similarities. Both emphasize the use of market
knowledge, benefit from integration, and highlight the importance of lead time
compression, however, for different reasons. In a lean supply chain, lead time
compression is an attempt to eliminate waste, whereas in a responsive supply
chain the goal is to improve responsiveness.323 Despite these similarities,
fundamental differences include different objectives (minimizing cost vs.
maximizing service level), complexity (high vs. low), focus (resource allocation
vs. responsiveness), or lead time (short vs. long).324

This has motivated the positioning of strategic management decisions outside
the core SCM model as depicted in Figure B-6 in section B.I.3.c. The core
elements of coordination, collaboration and integration are dependent on the
strategic position of the supply chain.325 This strategic position is determined by
the appropriate competitive priorities, the supply chain structure, and the specific
SCM processes.326 Only after decisions in these areas are made – under
consideration of the SCM framework and the potential it provides – that the
remaining aspects of the SCM framework can be aligned and matched.

2. Processes and Structures in Supply Chains
2.a. Adopting a Process View of Supply Chain Operations
One of the core principles of SCM is that of looking at a system – the supply chain

– in a systemic and holistic way. Thus, not only individual elements are
monitored, managed and optimized, but an entire system of elements, i.e.
companies and strategic business units. All companies of a supply chain network
are connected by at least one of the three flows consisting of information,
material, and financials; mostly by all three. These flows are essentially already
processes, though in a very unspecified form. More importantly, internal processes
are linked across supply chains through these three flows. Consequently, internal
business processes become supply chain business processes.327
323
Cf. Naylor, Naim and Berry: Leagility: Integrating the lean and agile manufacturing
paradigms in the total supply chain, pp. 109-110.

324
See for example Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing
the supply chain: Concepts, strategies, and case studies, p. 127 or Krajewski and
Ritzman: Operations management, pp. 420-422.

325 Supply chain and not company it is, because different supply chains can exist in one
company. This issue will be picked up in section B.IV.1.
326 The optimal supply chain structure also depends on competitive priorities, but often is
harder to change, i.e. it is often reflected as industry structures.
327 Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, p. 2.


Holistic View of Supply Chain Management

A process can be defined as a specific ordering of work activities across time
and place, with a beginning, an end, and clearly defined inputs and outputs.328 In
that sense, a set of activities are taken together for a specific purpose and form a
process.329 Since focus lies on the outcome, the customer orientation is explicitly
taken into account.330 This view is prerequisite for a systematic process analysis,
process improvement, and process reengineering.

It has been pointed out before that business processes are of particular strategic
importance. Innovations in business processes outlast product innovations because
they are much less transparent and more complex and therefore harder to
imitate.331 And even if individual processes are copied by competitors, it is much
harder to match processes that involve several business partners.332 That processes
are considered to be strategic assets is visible in the fact that companies protect
business processes through patents, with Amazon.com being one of the first to do
so for their One-Click ordering process.333 Building an organization around
functions creates unnecessary time delays and buffer inventory, which are then
necessary at the interfaces, especially along supply chains. In order to avoid such
inefficiencies, a process-oriented organization is suggested by many.334 This does
not mean that functions are not important. Functional expertise is still needed
along the business processes. It serves as a valuable input for process designs and
execution.

The significance of business processes has been prominently promoted by
Hammer and Champy’s concept of business process reengineering.335 In an update

328 Cf. Davenport: Process innovation, p. 5.
329 Cf. Ittner, Christopher D. and David F. Larcker: The performance effects of process


management techniques, in: Management Science, Vol. 43 (1997), No. 4, April, p. 523.
330 Cf. McCormack, Kevin and Bill Johnson: Business process orientation, supply chain

management, and the e-corporation, in: IIE Solutions (2001), October, p. 34.
331 Cf. for example Kuhn and Hellingrath: Supply Chain Management: Optimierte

Zusammenarbeit in der Wertschöpfungskette, p. 90. In fact, Kuhn and Hellingrath put

special emphasis on business processes in their view of SCM, cf. Kuhn and Hellingrath:

Supply Chain Management: Optimierte Zusammenarbeit in der Wertschöpfungskette,

p. 101.
332 Cf. McCormack and Johnson: Business process orientation, supply chain management,
and the e-corporation, p. 37.
333 Cf. McCormack and Johnson: Business process orientation, supply chain management,
and the e-corporation, p. 33.
334 Cf. Christopher: Logistics and supply chain management: Creating value-adding
networks, pp. 177-178.
335
See Hammer, Michael: Reengineering work: Don't automate, obliterate, in: Harvard
Business Review, Vol. 68 (1990), July/August.


Improving Supply Chain Performance

to their seminal work, Hammer has stated: “Streamlining inter-company processes
isn’t just an interesting idea: it’s the next frontier of efficiency.”336

Process management, however, goes beyond efficiency. Processes are also
important for the strategic differentiation between push systems and pull systems,

i.e. an efficient supply chain setup and a responsive supply chain setup.
Consequently, the nature of the processes supporting each of the two can be
distinguished as (1) push processes for efficient supply chains, and (2) pull
processes for responsive supply chains. Push processes are designed so that an
actual customer order triggers the process, and pull processes are designed in
anticipation of customer orders.337
Although authors have defined SCM processes in different ways, they are not
fundamentally different from what Figure B-8 shows. They all have in common
the basic understanding of a holistic process management approach in line with
the overall SCM concept. In addition, they all point out the need for intercompany
cooperation as all processes exceed one company’s boundaries. In terms
of detailed process definitions, the SCOR model stands out. Because of its
increasing acceptance in and significance for supply chains, it is described in more
detail in the following section.

336 Cf. Hammer: The superefficient company, p. 91.
337 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,


p. 8 and p. 14.

Holistic View of Supply Chain Management

Tier 4
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Figure B-8: Different views on SCM processes338

338
These process categories can be found in Lambert, Cooper and Pagh: Supply chain
management: Implementation issues and research opportunities, pp. 8-9, Mentzer et al.:
Defining supply chain management, Christopher: Logistics and supply chain
management: Creating value-adding networks, pp. 177-178, Chopra and Meindl:
Supply chain management: Strategy, planning, and operation, p. 17, and n.a.: Supply
chain operations reference model version 7.0, 2005, p. 2.


Improving Supply Chain Performance

2.b.
Connecting Supply Chain Management Processes with the Supply Chain
Operations Reference (SCOR) Model
Since supply chain business processes cross business unit and company
boundaries, it is necessary to connect those processes as seamlessly as possible. In
order to achieve this, a standardized process model is needed to avoid
misunderstandings and interface problems. Therefore, in 1996, the Supply Chain
Council (SCC) was organized by the consulting company Pittiglio Rabin Todd &
McGrath (PRTM) and AMR Research and initially included 69 voluntary
practitioner companies that met in an informal consortium. Then, as an
independent, not-for-profit, global corporation, the SCC started to develop the
SCOR model.339 Today, the SCC has more than 800 members and maintains
chapters worldwide. Membership is open not only to companies, but also to other
organizations and institutions, including universities. The following descriptions
are all based on documents, releases, presentations, and announcements issued by
the SCC and the original SCOR model description in version 7.0.340

As a process-reference model, SCOR integrates the ideas of business process
reengineering, benchmarking, and process measurement into one cross-functional
framework. It contains standard descriptions of management processes, a
framework of relationships among the standard processes, standard metrics to
measure process performance, management practices that produce best-in-class
performance, and standard alignment to features and functionality. The benefit of
such a reference model is that it can be implemented purposefully, described
unambiguously and communicated, managed, and revised to specific purposes.

Since SCOR can also be characterized as a total supply chain process model, it
includes all customer interactions, all product transactions from the supplier’s
supplier to the customer’s customer, and all market interactions from demand
understanding to order fulfillment. It is suitable for narrow supply chains within
one facility and also for more complex supply chains that span several tiers, as
illustrated in the SCOR model overview in Figure B-8. It explicitly excludes,
however, processes from the areas of sales and marketing, research and
technology development, product development, and elements of post-delivery
customer support. Issues of training, quality, information technology, and non-
SCM administration are not explicitly addressed, but implicitly considered.

The SCOR model is structured hierarchically in four levels, with the first three
levels being part of the SCOR model and the fourth level (and lower levels) being
outside its scope. Table B-4 provides an overview of the SCOR model structure.

339 In its ninth revision, the latest version 7.0 was officially introduced in April 2005.
340 Cf. n.a.: Supply Chain Council, Inc.: http://www.supply-chain.org, 2005, retrieved on:

February 15, 2006; and n.a.: Supply chain operations reference model version 7.0 for

more detailed information. The following descriptions of the SCOR model are collected

from various sources, all issued by the SCC, if not stated otherwise.


Holistic View of Supply Chain Management

Table B-4: Hierarchical structure of the SCOR model

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On level one, five process types define the scope and content of the SCOR
model, i.e. the major management process types plan, source, make, deliver, and
return. The enable process as a sixth one is generally excluded because it serves
as an enabling process for the five other management processes plan, source,
make, deliver, and return. As such, it only exists in conjunction with these five.
The five core management processes are characterized by planning, executing, and
enabling. Plan processes have the purpose of aligning expected resources to meet
expected demand requirements. Execution processes – source, make, deliver,
return – are triggered by planned demand or actual demand and change the state
of material goods. These processes generally involve scheduling, product
transformation, and transportation. Enabling processes prepare, maintain, or
manage information or relationships, upon which the planning and execution
processes rely.

Level two splits the process types into process categories. A company’s supply
chain can be configured based on 30 process categories.341 Figure B-9 provides an
overview.

341 Note that though the enable process has nine basic process categories, these change in
content for each process type, essentially resulting in five times nine process categories.


Improving Supply Chain Performance

Plan (5 categories) P1 Plan Supply Chain
P2 Plan Source P3 Plan Make P4 Plan Deliver P5 Plan Return
Source (3 categories) Make (3 categories) Deliver (4 categories)
S1 Source Stocked Product
S2 Source Make-to-Order
Product
S3 Source Engineer-to-Order
Product
M1 Make-to-Stock
M2 Make-to-Order
M3 Engineer-to-
Order
D1 Deliver Stocked
Product
D2 Deliver Make-to-
Order Product
D3 Deliver Engineerto-
Product
D4 Deliver Retail
Product
Source Return
(3 categories)
SR1 Source Return
Defective Product
SR2 Source Return
MRO Product
SR3 Source Return
Excess Product
Deliver Return
(3 categories)
DR1 Deliver Return
Defective Product
DR2 Deliver Return
MRO Product
DR3 Deliver Return
Excess Product
Enable (9 categories)
1) Establish and manage rules 5) Manage capital assets 9) Process specific elements
2) Assess performance 6) Manage transportation
3) Manage data 7) Manage supply chain configuration
4) Manage inventors 8) Manage regulatory compliance


Figure B-9: Overview of all SCOR process categories, according to the SCOR model

On level three, process categories are further divided into process elements.
Here, each process element is described and defined in detail. The definition
includes the linkage to performance attributes, best practices for each process
element, if identified, and inputs and outputs of the process element. All in all, the
SCOR model in version 7.0 carries 177 process elements.

The following five performance attributes are identified by the SCOR model:

(1) reliability, (2) responsiveness, (3) flexibility, (4) costs, and (5) assets. The
performance attributes are defined as follows:342
-
Supply chain reliability. The performance of the supply chain in
delivering the correct product to the correct place, at the correct time, in
the correct condition and packaging, in the correct quantity, with the
correct documentation, to the correct customer.

-
Supply chain responsiveness. The speed at which a supply chain provides
products to the customer.

342 n.a.: Supply chain operations reference model version 7.0, p. 7.


Holistic View of Supply Chain Management

-
Supply chain flexibility. The agility of a supply chain in responding to
marketplace changes to gain or maintain competitive advantage.

-
Supply chain costs: The costs associated with operating the supply chain.

-
Supply chain asset management. The effectiveness of an organization in
managing assets to support demand satisfaction. This includes the
management of all assets: fixed and working capital.

Performance attributes are organized hierarchically; therefore performance
metrics identified in process elements are linked to these top level performance
attributes. The SCOR model defines 144 performance metrics in total and includes
even more with the process element descriptions. They are all linked to one of the
five performance attributes described above. This is similar to existing
hierarchical performance systems such as the DuPont system or the strategic
Balanced Scorecard cause-and-effect map.343

Since the SCOR model aims to be independent from specific industries, no
processes beyond the third level are defined. Instead, companies can implement
their own management practices and processes. In fact, the operational
implementation only takes place on level four and below. Managerial
implementation of the SCOR model, however, takes place from level one to level
three. In order to document processes on the lower levels, classical process
decomposition is used. It is also explicitly pointed out by the SCOR model that
competitive advantages by means of business processes are achieved on the
operational implementation levels, i.e. level four and below.

The SCOR model provides several benefits. Especially attractive are the
standardized processes of the model that enable a common language between
supply chain partners. This ensures a better compatibility and the realization of
synergies within partner nets.344 The standardized process elements also provide a
framework of relationships between the processes.345 This is supported by Gosain,
Malhotra, and El Sawy’s call for business standards: “These standards need to be

343
For the DuPont system of analysis, see for example Gitman, Lawrence J.: Principles of
managerial finance, 11th ed., Boston 2006, pp. 75-77; and Kaplan, Robert S. and David

P. Norton: Linking the balanced scorecard to strategy, in: California Management
Review, Vol. 39 (1996), No. 1, p. 71.
344
Cf. Werner: Supply Chain Management: Grundlagen, Strategien, Instrumente und
Controlling, pp. 26-27; Huan, Samuel H., Sunil K. Sheoran and Ge Wang: A review
and analysis of supply chain operations reference (SCOR) model, in: Supply Chain
Management: An International Journal, Vol. 9 (2004), No. 1, p. 24; and Kuhn and
Hellingrath: Supply Chain Management: Optimierte Zusammenarbeit in der
Wertschöpfungskette, p. 108.

345
Cf. Huan, Sheoran and Wang: A review and analysis of supply chain operations
reference (SCOR) model, p. 24.


Improving Supply Chain Performance

not merely technical but should include support for business rules, contracts,
procedures for dispute resolution, and so on.”346

Another benefit of the model is that implementation of the SCOR processes in
a company forces that company to deal with its current practices and therefore
also provides a framework for process reengineering initiatives.347 In addition, the
comprehensive set of performance metrics and the model’s broad and growing
acceptance are seen as benefits of the SCOR model.348

Despite the benefits the SCOR model provides, some critical remarks and
shortcomings should be considered. Werner identifies four disadvantages of the
SCOR model:349

-
High level of abstraction.

-
Requires a certain degree of continuity.

-
Increases dependencies among supply chain partners.

-
Close relationships lead to the revelation of sensitive information and the
loss of know-how.

Besides Werner’s criticism about the high level of abstraction, the identified
concerns are not unique to the SCOR model but rather apply to any kind of close
supply chain relationship. Furthermore, the observation that the level of
abstraction is too high can also be questioned because the process elements
defined in the SCOR model do provide a certain degree of detail, especially in
conjunction with the process and performance metric definitions and best practice
descriptions.

A more common concern is that the standardization of business processes
might lead to competitive disadvantages. As such, business processes become the
norm and companies have a harder time distinguishing themselves from the
competition.350 This is certainly a valid remark. The SCOR model, however,
explicitly recognizes this concern by pointing out that companies should seek
competitive advantages on the implementation levels of the SCOR model, i.e. the

346
Gosain, Malhotra and El Sawy: Coordinating for flexibility in e-business supply chains,

p. 33.
347 Cf. Werner: Supply Chain Management: Grundlagen, Strategien, Instrumente und
Controlling, pp. 26-27.
348 Cf. Kuhn and Hellingrath: Supply Chain Management: Optimierte Zusammenarbeit in
der Wertschöpfungskette, pp. 108-109.
349 Cf. Werner: Supply Chain Management: Grundlagen, Strategien, Instrumente und
Controlling, pp. 26-27.
350
Cf. Göpfert: Einführung, Abgrenzung und Weiterentwicklung des Supply Chain
Managements , p. 39.


Holistic View of Supply Chain Management

levels where the SCOR model does not define processes. At these levels,
management and business processes are company-specific and supply chain-
specific. The question remains whether this is sufficient to provide a competitive
advantage or if such competitive advantages can only be achieved on higher levels
in the hierarchy.

Meyr, Rohde, and Stadtler have suggested a more detailed typology on the
second process level, the process category level. They distinguish between
functional attributes (procurement type, production type, distribution type, sales
type) and structural attributes (topography of a supply chain, integration and
coordination).351 These are important remarks because they point out an important
limitation of the SCOR model. It assumes that the supply chain and its members
are known. No guidance is provided with regard to the definition of the overall
supply chain network structure or its identification. Therefore, the structural
attributes are neglected.

This leads to another observation. The SCOR model is a comprehensive
operations model. It mainly supports operational processes. By explicitly leaving
out issues of sales and marketing, and thus demand management, technology
development, and product and process design, important aspects that are of high
relevance for the supply chain are not considered. It really focuses on the
operational perspective. For its intended scope, however, it is suitable and
comprehensive.

Since the SCOR model is a reference model, it provides improvement
suggestions based on identified best practices, but not optimization procedures.
Consequently, its “optimal” application remains vague. Moreover, there are no
explicit trade-off or risk considerations incorporated in the model. It is left to the
user of the SCOR model to take these into account individually throughout the
implementation process. Identified best practice processes, however, provide
valuable input for process and supply chain design decisions.

In the next section, a formal model distinct from the SCOR model is
developed, which addresses some of the limitations identified above. It should not
be seen separately from the SCOR model, but rather as complementary to it. It
mainly addresses structural considerations that are neglected in the SCOR model.
Furthermore, a mathematical representation serves to illustrate important supply
chain issues, such as supply chain design, supply chain added value, supply chain
roles, and conflicting supply chain objectives.

351
Cf. Meyr, Herbert, Jens Rohde and Hartmut Stadtler: Basics for modelling, in: Stadtler,
Hartmut and Christoph Kilger (Eds.): Supply chain management and advanced
planning: concepts, models, software and case studies (2nd ed.), Berlin Heidelberg New
York 2002, pp. 54-61.


Improving Supply Chain Performance

2.c. A Formal Framework for Supply Chain Structures
The systemic and holistic view of supply chains makes it necessary to take a
complex structure of linked companies into consideration. It is not sufficient to
only focus on one company anymore. A commonly acknowledged definition of a
supply chain has been provided by Christopher: “The supply chain is the network
of organizations that are involved, through upstream and downstream linkages, in
the different processes and activities that produce value in the form of products
and services in the hands of the ultimate customer.”352 Additionally, Christopher
remarked that “…the task of managing, co-ordinating and focusing this value-
creating network might usefully be termed supply chain orchestration.”353
Therefore, it is of high importance to first identify the relevant supply chain
structure in order to make sound SCM decisions. This has often been neglected in
literature and most authors seem to assume that everyone knows who a member of
the supply chain is and how they are connected.354 Therefore, a model is needed
that assists in mapping supply chain networks.355 Based on such a model, a
mathematical representation is developed that identifies all supply chain network
elements and that defines an objective function formulation for overall supply
chain profitability.

An approach suggested by Walker is used as foundation in order to derive such
a model that can serve as a basis for supply chain mapping.356 In addition,
Lambert, Cooper, and Pagh have also suggested a model for supply chain network
structures that provides additional valuable input.357 Under Walker’s approach,
first the main thread in a supply chain should be identified. Mostly, the main
thread constitutes the physical flow that carries the most added value.358 In case of
a service supply chain network, it might be an information flow as well. The
emphasis lies on the value added by it. It can also be, however, a crucially

352
Christopher: Logistics and supply chain management: Creating value-adding networks,

p. 17. Shawn uses the term business units instead of companies, pointing out the
difference, cf. Shaw: Information-based manufacturing with the Web, p. 117.
353 Christopher: Logistics and supply chain management: Creating value-adding networks,

p. 293.
354 Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, p. 4.
355 This need was also identified as a research question by Lambert, Cooper and Pagh:
Supply chain management: Implementation issues and research opportunities, p. 14.
356
See Walker, William T.: Unbundling the corporation: A blueprint for supply chain

networks, in: McCormack, Kevin and William C. Johnson (Eds.): Supply chain

networks and business process orientation,Boca Raton, FL 2003, pp. 103-129.

357
See Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 1-19.
358 Cf. Walker: Unbundling the corporation: A blueprint for supply chain networks ,
pp. 104-105.


Holistic View of Supply Chain Management

important link within the network without adding much monetary value. The rest
of the network is built around the main thread. As a starting point, Figure B-10
shows a simplified supply chain for an automobile manufacturer with an identified
main thread. Those positions in the supply chain that provide value along the main
thread are shaded grey. Though simplified, the underlying structure resembles a
typical supply chain in this industry.

Automobile
Manufacturer
Raw
MaterialRaw
Material
SteelproducerSteel
producer
Engine
Manufacturer
Engine
Electronics
Indirect MaterialSupplierIndirect Material
Supplier
SteelProducerSteel
Producer
MetalSheetsMetal
Sheets
Component
Supplier B
Component
Supplier A
MaterialSupplier AMaterial
Supplier A
MaterialSupplier BMaterial
Supplier B
MaterialSupplier CMaterial
Supplier C
Europe DC
US DCUS DC
DC = distribution center
main thread
Asia DCAsia DC
EndCustomerEnd
Customer
EndCustomerEnd
Customer
EndCustomerEnd
Customer
Figure B-10: Simplified supply chain network structure

The main thread indicates a sequential throughput. This is important for the
formal model developed later on as it allows the division of the supply chain
network into tiers. There is always at least one main thread position in each tier
and main thread positions are connected to each other, i.e. a main thread position
in a supply chain network cannot exist without being connected to all main thread
positions through at least one other main thread, be it in a previous or in a
following tier.359 Instead of identifying main thread positions, Lambert, Cooper,
and Pagh have distinguished between four types of business process links between
supply chain partners: (1) managed process links, (2) monitored process links, (3)
not-managed process links, and (4) non-member process links.360 Delfmann and
Albers have added commonly managed process links as another type that is
positioned between managed and monitored process links.361 Commonly managed
process links are those that are jointly managed by the affected companies. One
shortcoming of Lambert, Cooper, and Pagh’s perception of business process links

359 Based on the definition of the model, no within-tier linkages exist.
360 Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and


research opportunities, pp. 7-8.
361 Cf. Delfmann and Albers: Supply chain management in the global context, p. 37.


Improving Supply Chain Performance

is that they take the perspective from one company, what they call the focal
company. The focal company can be any company within the supply chain
network and therefore the structure looks different for each company.362 This is
not a desirable condition since the purpose is to capture an entire supply chain
network with as little ambiguity as possible. It is otherwise difficult to provide
assistance to optimize decisions within an entire supply chain as implied by the
holistic and systemic nature taken at the normative level of SCM.

Another important factor is the identification of a supply chain driver, or, as
Walker and McCormack and Johnson have put it, an orchestrator.363 The supply
chain driver is the member of the supply chain, which “owns” the key value to the
supply chain.364 For example, if a manufacturer holds a patent for a certain
technology that defines the product or service, then this manufacturer possesses
the most power within the supply chain. Consequently, this partner is responsible
for taking the lead in defining the overall supply chain strategy. In case access to
the customer is crucial, companies holding the key to customers constitute a
relatively high control power over the supply chain, as it is the case for example
for mail-order companies, department stores, discounters, hardware stores, or
electronics superstores. It could also be that the supply chain driver is not even
part of the physical supply chain, as in the case of Red Bull.365 Red Bull only
possesses its marketing power and drink recipe. The bottling and selling is done
by supply chain network members that are independent of Red Bull. Still, Red
Bull clearly drives its supply chain. Being aware of the supply chain driver is
important when it comes to determining the key player in aligning and
coordinating the supply chain to maximize total supply chain profitability. Pareto


362
Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 6-7.

363
Cf. Walker: Unbundling the corporation: A blueprint for supply chain networks , p. 108
and McCormack and Johnson: Supply chain networks and business process orientation:
Advanced strategies and best practices, pp. 3-4. The need for such a supply chain leader
is also identified by Langemann, Timo: Collaborative Supply Chain Management, in:
Busch, Axel and Wilhelm Dangelmaier (Eds.): Integriertes Supply Chain Management:
Theorie und Praxis effektiver unternehmensübergreifender Geschäftsprozesse (2nd ed.),
Wiesbaden 2004, p. 442. Mentzer et al. see it even as a necessity to have a supply chain
leader and compare it to a “channel captain”, cf. Mentzer et al.: Defining supply chain
management, p. 14.

364
Reasons for such a strong supply chain position can be size, economic power,
proprietary technology, customer patronage, comprehensive trade franchise, or the
initiation of the inter-firm relationships, cf. Mentzer et al.: Defining supply chain
management, p. 14; and Walker: Unbundling the corporation: A blueprint for supply
chain networks, p. 109.

365
Red Bull is a highly successful Austria-based energy drink company. Sales grew in
2004 by 32.3% to € 1,668 Mio.


Holistic View of Supply Chain Management

efficient results are not desired and the allocation of profits and losses should be
performed by the orchestrator.366 This supply chain driver corresponds to what is
referred to by some in the literature as hub firm or focal company.367

A distinction can also be drawn between hierarchic coordination and
autonomic coordination. Hierarchic coordination refers to the supply chain
orchestrator concept, whereas an autonomic coordination refers to market
mechanisms such as price, temporary partnerships, values, or marketplaces. In a
hierarchic supply chain setting, coordination can be achieved by direct order or by
strategic alignment of dependent companies. Since the supply chain leader most
likely cannot communicate directly with all tiers of the supply chain, the tiers in
between serve as communicators of the message. Furthermore, programs and
plans can serve as guidelines for all supply chain members.368 Williamson has
added the hybrid form of coordination positioned in between market and hierarchy
and also remarked that positive factors might exist that benefit all three forms.369

The last key aspect of Walker’s framework has been the explicit modeling of
physical, information, and financial flows as planes.370 These planes represent an
explicit view of physical, information, and financial flows by separating them
visually into three horizontal layers. By looking at them individually from a
business process perspective, the linkages become clear while still being distinct.
Within a company, they affect each other but between companies, each flow links
with the corresponding one at the connected company. Figure B-11 illustrates this
for the previously depicted, simplified supply chain network. This network is
aligned according to the main (physical) thread. Information and financial flows,
however, can spread among all supply chain members, depending on the policies
to which they are exposed. This is omitted at this point, but the possibility is
considered later in the mathematical model.

Based on the presented framework, a formal model can now be developed that
helps identify all supply chain network elements and helps define an objective
function formulation for overall supply chain profitability. The advantage of such

366 Cf. Busch and Dangelmaier: Integriertes Supply Chain Management -ein

koordinationsorientierter Überblick , pp. 12-20.
367 Cf. Delfmann and Albers: Supply chain management in the global context, p. 34.
368 Cf. Busch and Dangelmaier: Integriertes Supply Chain Management -ein

koordinationsorientierter Überblick , pp. 12-20.
369 See Williamson: Comparative economic organization: The analysis of discrete

structural alternatives. There, it is pointed out that the hybrid form is more than just

merely a compromise of market and hierarchy organization, but has its own, unique

position in between the other two.
370 Cf. Walker: Unbundling the corporation: A blueprint for supply chain networks,

pp. 110-111.


Improving Supply Chain Performance

a formal mathematical model is that it provides a higher level of precision.371 A
general framework is derived that has to be adapted to the requirements of the
specific supply chain network under consideration. When applied, the model
design depends highly on the individual supply chain network. One should
therefore follow a contingency approach when applying it. Consequently, as with
any model, it is of crucial importance that the system boundaries be chosen
carefully. As a narrow definition of the supply chain boundaries can help
companies get started, a total supply chain view offers the greatest potential while
also requiring the most resources.372 Lambert, Cooper, and Pagh have
differentiated in this context between primary and supportive members or
processes. Companies or business units who actually perform operational or
managerial activities to produce a specific output for a particular supply chain are
considered to be primary members of the supply chain. Supporting members, in
contrast, simply provide resources for the primary members in form of services,
knowledge, utilities, or assets.373

371
Cf. Schmenner and Swink: On theory in operations management, p. 100.

372
Cf. Poirer: Achieving supply chain connectivity, p. 17.

373
Cf. Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, pp. 5-6. About choosing system boundaries in more general
terms, see for example Forrester, Jay W.: Principles of systems, second preliminary
edition, 2nd ed., Cambridge, Massachusetts 1969, pp. 4.1-4.5.


Holistic View of Supply Chain Management 91
Raw
Material
Steel Producer
Indirect Material
Supplier
Engine
Electronics
Material
Supplier A
Material
Supplier B
Material
Supplier C
Steel
Producer
Engine
Manufacturer
Component
Supplier A
Component
supplier B
Metal Sheets
Europe DC
US DC
Asia DC End
Consumer
End
Consumer
End
Consumer
financial flow layer
physical flow layer
information flow layer
Automobile
Manufacturer
Figure B-11: Simplified supply chain network, illustrating physical, information, and
financial flows

Improving Supply Chain Performance

A supply chain network should be defined by its main product or service,
because ultimately, each and every individual product or service has its own
supply chain. Obviously, it is not feasible to model such a view. Even considering
an aggregate view of a single product might be too narrow a focus. It seems to be
suitable rather to form homogeneous product groups that generally share the same
supply chain. For example, there are many different VW Golf models available,
with diesel or gasoline engine, as convertible, station wagon or sedan and so forth.
It is appropriate to build a homogeneous product group “Golf” instead of a group
for each model variation. The main factor that should be considered when
including several model variations is the implied changes necessary within a
supply chain for each model. If the supply chain network structure changes
significantly by including more product variations, they should not be included.374
The total value created by such a defined supply chain network over a period of
time can generally be described as follows:

 [1]: X = s - v

Pt PtPt

with XPt = total value created in period t by the supply chain network of
product P that defines the supply chain network
sPt = total sales revenue of product P in period t
vPt = cost of material and services procured from outside the supply
chain network in period t

In order to determine the span of value creation a supply chain network covers,
a supply chain depth indicator XP. can be calculated in the following way:

Pt

[2]: X = 1- v

P.

s

Pt

According to the supply chain network model, i=1,…,n tiers between the
source and the end of the supply chain can be identified. The tiers are aligned
according to the main thread. Each tier i holds j=1,…,mi positions for companies
that contribute and add value to this supply chain network. Figure B-12 illustrates
this structure.

374 See Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 41.

Holistic View of Supply Chain Management

D
DDC
CC
=
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i=
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43Y44Y45Y46
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33Y*
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11Y12Y13
RawMaterialRawMaterial
Figure B-12: Supply chain network structure, including position notions


Improving Supply Chain Performance

The total value creation XPt of a supply chain network is therefore the sum of
the added values of all positions. Therefore, Equation [1] can be stated more
detailed as follows:

n
mi

[3]: X Pt = SS(X Pt )ij

i=1
j=1

with XPt = total value created by the supply chain network of product P that
defines the supply chain network, in period t
(XPt)ij = incremental added value of position ij for product P in period t
i = supply chain network tier, i = 1 (customer tier), … , n (point of
origin)
j = position within tier i, j=1, … , mi
(Y*P)ij indicates a main thread position in Figure B-12

Starting the count at the tier closest to the customer is sensible since all supply
chain activities focus on the end customer. To reflect this focus, the first tier
should always be the one closest to the end customer. Mostly, the (physical) main
thread routes through some kind of OEM. This OEM can serve as an orientation
point for a supply chain network since the network “funnels” through this tier.
Often, the OEM tier consists only of the OEM position.

The beginning of the scope of the supply chain network depends on the
specific supply chain constellation. The (functioning and efficient) markets for
raw materials are often a good starting point. The raw material tier should only be
included if it represents a raw material that is of particular relevance for the supply
chain. Commodity products can be excluded from the supply chain network
perspective if an efficient supply is ensured and/or it is not particularly important
or critical to the product. In some instances, it can make sense to go beyond these
market boundaries in order to secure supply. From Equation [1], it becomes clear
that in the extreme case of including all companies connected to a supply chain,
there would no longer be any outsiders to the supply chain and a total integration
would be achieved, with vt being zero. End customers are not included in the
supply chain network. In case a monopoly-like company controls the access to end
customers, these companies could be also excluded, if they do not add significant
value to the supply chain. However, in line with the SCM framework, also end
customers can be an active part of the supply chain and therefore then represent
the first tier.375 Even if excluded, they build an important link to the first tier that
has to be considered accordingly. Figure B-13 illustrates the overall view of
supply chain network tiers.

375
For example, Mentzer et al. consider the final consumer to be part of the supply chain,
cf. Mentzer et al.: Defining supply chain management, p. 4.


Holistic View of Supply Chain Management

raw material end consumer


Tier i=8 Tier i=6 Tier i=4 Tier i=2
Tier i=7 Tier i=5


Tier i=3 Tier i=1


OEM
If source is relevant, the raw Importance depends on Customer oriented business
material layer may be supply chain driver. environment. Sometimes
included. This layer often

replaced by monopoly-like

consists of commodity goods. company position. Then, these
In such a case, a market monopoly companies are "in
coordination structure is control" over customer.

indicated (rather transaction
oriented). In the end, inclusion
depends on importance.


Figure B-13: Tier depiction of a supply chain network

From this, we derive the objective function for supply chain networks. One
important aspect of SCM is to maximize total supply chain profitability.376
However, profitability can have many different meanings, as can be the objectives
of a supply chain.377 In the model proposed, it is assumed that long-term
profitability of the supply chain network should be maximized. While there are
various profitability performance measures possible, the Net Discounted Cash
Flow (NDCF) for k periods – the planning horizon – is chosen as an appropriate
profitability measure. Nevertheless, other profitability or performance measures
could also be used to determine supply chain objectives. The objective here,
therefore, is to maximize the sum ZP of all supply chain network participants’
individual NDCFs.

n mi

[4]: ZP = SS(NDCFP )ij .. max! for k periods

i=1 j=1

with (NDCFP)ij = Net Discounted Cash Flows of position ij in the supply chain
network of product P, with a planning horizon of k periods

As can be seen in Equation [4], total supply chain profitability depends on the
profits made by all supply chain members. As profit sharing agreements are very
difficult to achieve and therefore almost non-existent, each member aims to
maximize its own profits independently. Furthermore, companies often participate
in more than one supply chain, which complicates matters further. Each company
U’s profitability function can be described as the sum of the profits made with all
products and supply chains of this company U. It can be described as follows:

376 See for example Chopra and Meindl: Supply chain management: Strategy, planning,
and operation, p. 6.
377 For a discussion on supply chain objectives, see section B.I.3.a.


Improving Supply Chain Performance

lU
nmi

[5]: Z =
p
(NDCF *c ()]

U
SS.
P )ij U [YP ij .. max! for k periods
P=p1 i =1 j =1

with cU[(YP)ij] = 1, if company U fills position ij in product P’s supply chain
network, 0 otherwise
P = product or product group P with P=p1, … , plU (lU products
company U is involved in)
where (YP)ij represents the supply chain position ij for product P

It can be observed that Equations [4] and [5] are only partly linked to each
other. In general, only parts of the objective function of company U are also
elements of the objective function of the supply chain network for a specific
product P. In case company U is involved in more than one supply chain network,
only parts of the objective function of the supply chain network for a particular
product P are also elements of the objective function of company U. Therefore,
without a perfect incentive policy, solutions are likely to be sub-optimal.378

Up to this point, the influence of the actual structure of a supply chain on
effectiveness and efficiency of the entire supply chain network has not been
formally reflected. Even if all participants of a supply chain network acted in the
best interest of the entire supply chain, profitability might be limited due to the
structure, i.e. the linkages between supply chain members. Changing the structure
of a given network can move the efficient frontier outwards, i.e. expanding the
effectiveness and efficiency potential of the network.379 Consequently, this
structure has to be formally represented as well. To achieve this, physical,
information, and financial flow connections are identified as follows:

[6]:
MY Y := 1, if physical flow between (YP)ij and (YP)i’j’ exists, 0

()()

Pij
Pi ' j '

 otherwise
with i.i’, if j=j’ and j.j’, if i=i’


[7]:
IY := 1, if information flow between (YP)ij and (YP)i’j’ exists, 0

Y

P

()()ij
Pi ' j '

 otherwise
with i.i’, if j=j’ and j.j’, if i=i’


378
For a general discussion of incentive issues, see Narayanan, V. G. and Ananth Raman:
Aligning incentives in supply chains, in: Harvard Business Review, Vol. 82 (2004),
November, pp. 94-102.

379
Cf. for example Chopra and Van Mieghem: Which e-business is right for your supply
chain?, pp. 33-34; and for a more detailed discussion, see section B.I.3.a


Holistic View of Supply Chain Management

[8]: FY := 1, if financial flow between (YP)ij and (YP)i’j’ exists, 0

Y

()()Pij Pi ' j '

 otherwise
with i.i’, if j=j’ and j.j’, if i=i’


According to the model, the variables M, I, and F for physical, information,
and financial flows merely define whether a flow exists or not. In case a flow
exists, it is also important to define the characteristics of this flow both
quantitatively and qualitatively. Additionally, structural changes may be
established by changing connections within the existing supply chain network
members or by adding/removing positions to/from the network. Furthermore,
companies occupying certain network positions may also be changed. For all these
considerations, it is a prerequisite to first know the existing supply chain network
structure and linkages within it. For this purpose, the developed model above
provides a valuable framework.

In order to apply the formal model for supply chain structures developed in
this section, one has to investigate one specific supply chain environment. This
serves the requirement of a contingency approach as postulated on the normative
level of the SCM framework. Any given supply chain can be improved through
the process of deriving the necessary information in order to create the supply
chain map. The logical, formal connections can then be communicated easily and
different scenarios can be elaborated. Mapping several supply chains of similar
industries in the way described makes it also possible to identify best practice
supply chain structures. In the analysis to follow in sections C and D, such a level
of detail cannot be obtained through the available empirical database.
Nevertheless, the awareness raised through this analysis is of great importance to
evaluation of the analyses conducted later on. Other SCM analyses in the future
can make use of the formal framework and its implications in order to identify
best practice supply chain structures and to better understand the dynamics within
and between supply chains.

After having discussed strategic management decisions of SCM above, the
next section provides an overview of the currently most significant strategic
physical and technical infrastructure toolset, namely e-business.

3. E-Business Enabled Supply Chain Management
3.a. From Information and Communication Technology to E-Business
The histories of IT and communication technology are quite complementary to
each other, with IT being the younger one of the two technologies. Network
technology is considered in the traditional terminology to be part of IT sciences,
with the Internet as the most prominent representative of networks. Though the
term “network” has a stong grounding in traditional communication technology,


Improving Supply Chain Performance

the Internet is considered to be a derivative of the computer and IT field.380 The
following section is intended to provide a brief historical review of the
circumstances that have led to the current status of IT and the Internet. This is
followed by a definition of the scope of e-business as an outcome of these
developments.381

It is deemed appropriate to give such a review in order to provide a better
understanding of the rather solid foundation of these developments and their
sustaining economic impact. In the field of SCM, many open issues remain in the
area of managerial integration of independent entities. Similar barriers of
integration existed in the beginning of the Internet, such as proprietary standards,
conflicts of interest, or legacy systems. These barriers have been overcome by the
Internet community. The historical developments as well as the current
management of the Internet may provide ideas that support initiatives for industry-
independent, total supply chain integration. Standardization is certainly one
ingredient to consider more closely.382 The SCOR model might serve as an
example for such an initiative, but it is only a starting point compared to the
necessary efforts that have been undertaken to make the Internet as successful as it
is.

IT is closely connected to the invention of computers and related technology.
Consequently, Zuse’s invention of the Z1 Computer that started in 1936 and
Aiken and Hopper’s Harvard Mark I Computer, originally termed Automatic
Sequence Controlled Calculator (ASCC) in 1944 can be considered to be the
starting points of information sciences. The invention of integrated circuits in
1959 represented another significant milestone in the advance of IT.383 Following

380
Cf. Leiner, Barry M. et al.: A brief history of the Internet, 2003,
http://www.isoc.org/internet/history/brief.shtml, retrieved on: February 15, 2006, p. 13.

381
There exist countless sources on the history of the Internet and IT developments.
Therefore, the section on the history of the Internet is based primarily on a summary
provided by the ones that actually did shape the Internet over the last decades, see
Leiner et al.: A brief history of the Internet; and a widely acknowledged and
comprehensive timeline of the history of the Internet by Hobbes, see Hobbes, Robert:
Hobbes' Internet timeline, version 8.1, 2005, http://www.zakon.org/robert/internet/
timeline/, retrieved on: February 15, 2006. Since Hobbes’ timeline is heavily peer-
reviewed, commented on and peer-authored, it is considered to be a highly reliable and
trustworthy source. Also, several mirrors of this page exist.

382
Standardization is also seen as an important enabler in other areas, for example
customization, see Swaminathan, Jayashankar M.: Enabling customization using
standardized operations, in: California Management Review, Vol. 43 (2001), No. 3,
Spring, pp. 125-135.

383
See Ceruzzi, Paul E.: Reckoners: The prehistory of the digital computer, Westport,
Connecticut 1983, pp. 10-72; and Ceruzzi, Paul E.: A history of modern computing,
Cambridge, Massachusetts 1998, pp. 178-179. These two sources also provide excellent


Holistic View of Supply Chain Management

these groundbreaking inventions, a steady but nevertheless relatively slow process
continued developing the field’s potential. From the early 20th century until the
years after the Second World War, transportation technology outpaced IT and
communication technology in terms of economic impact, with the emergence of
the aviation and automotive industry being the most significant representatives.384

For the development of IT, other major breakthroughs have been the invention
of Random Access Memory (RAM) in 1949 by Jay W. Forrester, which has been
practically used from 1952 on, dynamic Random Access Memory (DRAM) chips
in 1970, the first microprocessor in 1971 and the first consumer computers in the
mid-1970s.385 Then, in 1979, the first spreadsheet software, VisiCalc, was
introduced, followed by Rubenstein and Barnaby’s word processing software
WordStar. With these applications, computers began to deliver visible value for a
wider user audience. In 1981, the IBM home computer with MS-DOS as its
operating system was introduced and in 1985, Microsoft Windows followed.386
Computer capabilities in terms of processing power, memory size, hard disk
memory, graphic power and other performance and capacity measures continued
to grow and still do. Furthermore, prices dropped to levels at which a broader user
base was able to afford computers. Following the development of traditional
computers, the processing power and miniaturization of electronics spread to other
devices used for home entertainment, car controls, home appliances, industrial
machinery and a myriad of other purposes.

Communication technology has had an even longer history. Foundations were
laid by the first electromagnetic telegraphs in the mid 19th century and the
invention of the telephone in the late 19th century. As early as 1886, Sears first
sold watches via telegraph and hence telemarketing as well as Sears, Roebuck &
Co. was born. Only in 1930 did the telephone network outgrow the telegraph
network. The telephone network kept growing and phone technology kept
developing steadily, with further milestones being the invention of the fax
machine in 1960, the emergence of optical fiber wire technology in the mid-1960s
and the first cellular phone communication network in 1979 in Japan. Telephone
network technology, in its most basic sense, has also been the foundation of
computer network technology development.

overviews of the history of computing. See also the Computer history timeline,
http://www.computerhistory.org/timeline/, Computer History Museum, retrieved on:
February 15, 2006.

384
Cf. Delfmann and Albers: Supply chain management in the global context, p. 58.

385
See Ceruzzi: A history of modern computing, pp. 198-241. Despite his contributions to
systems theory, to many Jay W. Forrester might be better known as the inventor of
RAM.

386
See Brayton, Colin: Data visualization: A brief history, in: Securities Industry News,
Vol. 17, No. Issue 2005, p. 7 and p. 29; and n.a.: 1978: The revolution begins, in:
InfoWorld, Vol. 20, No. Issue 1998, pp. 3-35.


Improving Supply Chain Performance

In 1969, the ARPANET, the first packet-switching network, was launched by
the Advanced Research Projects Agency (ARPA).387 The ARPANET can be seen
as the incubator of the Internet as it is known today. The four founding nodes were
the University of California, Los Angeles, the Stanford Research Institute, the
University of California Santa Barbara and the University of Utah. Over the
following years, the number of nodes and users grew to more than 35 hosts and
approximately 2,000 users in 1973. The key developments in the ARPANET
environment have been the following:

-
1972, introduction of e-mail in the form as it still is used today.

-
1982, introduction of the TCP/IP388 network protocol, accompanied by
one of the first definitions of the Internet. It defined “[…] an ‘internet’ as
a connected set of networks, specifically those using TCP/IP, and
‘Internet’ as connected TCP/IP internets.”389 Work on the TCP/IP
protocol started in 1972 and the ARPANET switched to TCP/IP on
January 1, 1983.

-
1984, introduction of domain name system (DNS) makes it obsolete to
know the exact numerical location of a server and replaces the numerical
sequence by words.

-
1985, launch of the NSFNET program by the U.S. National Science
Foundation (NSF) with the intent to link the entire higher education
community together. TCP/IP was chosen as its network protocol. In the
following years, many sub-nets based on TCP/IP have been established.

-
1990, disconnection of the ARPANET.

The NSFNET program continued to facilitate the growth of the ARPANET by
coordinating Internet activities and providing physical infrastructure, such as the
data transmission backbone. New countries were connected every year. The usage
of the NSFNET backbone has been limited to research and education purposes. In
order to lower operating costs, regionally limited commercial use was permitted.
In 1988, the NSF started to develop plans to privatize and commercialize the
Internet.

Although operating on the basis of the same protocols, namely TCP/IP, the
Internet in the 1980s still consisted of separated networks and services. In 1989,

387
The underlying theory on packet switching networks was first published by Kleinrock,
Leonard: Information flow in large communication nets, in: RLE Quarterly Progress
Report (1961), July. ARPA has changed names further on back and forth and is now
referred to as the Defense Advanced Research Projects Agency (DARPA).

388 Transmission Control Protocol / Internet Protocol (TCP/IP).
389 Hobbes: Hobbes' Internet timeline, version 8.1, p. 6.


Holistic View of Supply Chain Management

Berners-Lee has started to develop the World Wide Web (WWW) at CERN390,
which was further refined together with Cailliau.391 One of the main
characteristics of the WWW has been the application of hypertext that allows for
referring to other documents and web pages by interactive links. As remarked on
CERN’s homepage: “The basic idea was to merge the technologies of personal
computers, computer networking and hypertext into a powerful and easy to use
global information system.”392 The key technologies behind the WWW have been,
and still are, the hypertext transfer protocol (HTTP), the hypertext markup
language (HTML) and uniform resource identifiers (URIs, also known as uniform
resource locators, URLs).

Because the development of the WWW became increasingly time consuming,
Berners-Lee asked other developers over the Internet to join the development
efforts. As a result, several browsers were programmed to make using the WWW
easier. In 1993, the first version of the Mosaic browser became available and
versions for personal computers (PC) and Macintosh were launched shortly after.
With easy to use browsers available for popular computer systems, as the PC and
Macintosh were, the WWW gained quickly in popularity. In 1993, 1% of Internet
traffic was conducted through the WWW and the known 500 web servers. By the
end of 1994, ten million users had already made use of the WWW and 10,000
servers were available.393 By 2004, almost one billion Internet users had been
identified.394

In order to further maintain and develop Web standards, the World Wide Web
Consortium (W3C) was founded. With Berners-Lee as the founding and current
director of the W3C, the inventor of the WWW still drives its development. The
W3C is jointly administered by the MIT Computer Science and Artificial
Intelligence Laboratory in the USA, the European Research Consortium for
Informatics and Mathematics in France and Keio University in Japan.395 In 1994,
the commercial use of the Internet was permitted and in 1995, the NSF completed
its privatization efforts and focused solely on its research network again. The main

390 Conseil Europeen pour la Recherche Nucleaire (CERN) or European Organization for

Nuclear Research, Geneva, Switzerland, http://www.cern.ch.
391 Cf. CERN's greatest achievements, http://www.cern.ch, CERN,retrieved on: February

15, 2006.
392 CERN's greatest achievements, section on “The World Wide Web”.
393 Cf. CERN's greatest achievements, section “history of the www”, p. 2.
394 Cf. n.a.: Internet usage statistics - the big picture, 2005, Internet World Stats,

http://www.internetworldstats.com/stats.htm, retrieved on: February 15, 2006 and n.a.:

Worldwide Internet users will top 1 billion in 2005, Computer Industry Almanac, Inc.,

press release September 3, 2004, http://www.c-i-a.com/pr0904.htm, retrieved on:

February 15, 2006.
395 Cf. About the World Wide Web Consortium, http://www.w3.org/Consortium/about


w3c.html, W3C,retrieved on: February 15, 2006, pp. 1-2.


Improving Supply Chain Performance

backbone operations were now run by interconnected network providers and the
WWW as it is perceived today was on the way.396 The WWW is only one service
on the Internet. Examples of other services are e-mail, with its simple mail
transport protocol (SMTP), and the file transfer protocol (FTP).

The continuing development of the WWW is mainly driven by the W3C for
WWW protocols and standards. The Internet is further developed by a cooperative
and mutually supportive relationship of the Internet Activities Board (IAB), the
Internet Engineering Task Force (IETF), and the Internet Society, which mainly
facilitates the work of the IETF.397 To illustrate the work performed at the W3C,
the following examples are given of standards the consortium established. Such
standards are the portable network graphics (PNG) format to provide an
independent graphic format, the cascading style sheets (CSS) to add styles to web
documents, HTML 4.0 as an extension of the original HTML formats to provide
richer content, or XML 1.0, which in 1998 laid the foundation of the forthcoming
developments of the extensible markup language (XML).398 New developments
comprise extensions of XML and the vision of a semantic Web.399

The W3C’s mission is “to lead the World Wide Web to its full potential by
developing protocols and guidelines that ensure long-term growth for the Web.”400
This mission is further refined to provide the Web to

-
everyone, regardless of culture or abilities;

-
everything, from high-end computers to mobile devices;

-
everywhere, from high to low bandwidth environments;

-
diverse modes of interaction, that is to say all kinds of interacting
technologies such as touch screen, pen, mouse, voice, assistive
technologies or computer to computer; and

-
enable computers to do more useful work, for example through advanced
data searching and sharing.401

This mission makes it clear that the Web aims to reach beyond current
applications and to provide a platform-independent application environment.
Although developments in mobile and wireless technologies require adjustments
of protocols and standards on both the network communication level and the

396 Hobbes: Hobbes' Internet timeline, version 8.1, p. 14.
397 Cf. Leiner et al.: A brief history of the Internet, p. 11.
398 Cf. About the World Wide Web Consortium, pp. 13-15.
399 For more information on the latest developments of the WWW, see W3C’s webpage,


http://www.w3.org.
400 About the World Wide Web Consortium, p. 1.
401 Cf. About the World Wide Web Consortium, p. 16.


Holistic View of Supply Chain Management

service level, the main characteristic of distributed networking remains the same.
This characteristic embraces a maximum reach through commonly accepted and
open standards. Furthermore, these standards need constant refinements and
adjustments, which are conducted based on the principles that incubated the
Internet and the Web. According to that, standards have to be open documents, be
freely available, and be developed in a collaborative effort. As Leiner et al.
remarked:

“The most pressing question for the future of the Internet is not how the
technology will change, but how the process of change and evolution itself is
managed. […] If the Internet struggles, it will not be because we lack for
technology, vision, or motivation. It will be because we cannot set a
direction and march collectively in the future.”402

Besides the above mentioned consortia, independent vendors provided
consortium-independent inventions to the Web. In 1995, Sun launched Java and
Javascript as platform-independent programming languages. In the same year,
RealAudio launched its streaming technology that allows for near real-time radio
broadcasts. In 1998, the MPG3 audio format, developed by the “Frauenhofer
Institut Integrierte Schaltungen”, arrived at the Internet, though it had been already
developed in 1987 and has been applied by the Motion Picture Experts Group
(MPEG) for video signals before. MPG3 files are considerably smaller than
original audio files without a perceivable loss of quality. Based on this, peer-topeer
file sharing became popular in 1999 through Napster, a highly controversial
file sharing application. After a heated debate, however, and the loss of several
law suits, claiming infringement of copyrighted materials, Napster stopped its
service. Another de-facto standard has become the sharing of documents based on
Adobe’s portable document format (PDF). The main advantages of the PDF
format are the customizable document quality and therefore file size and its readyto-
print format that displays documents the same way without ambiguity,
independently from operating system or application. The key to its position and
wide distribution has been that the required viewer has become freely available.
Based the postscript format, a professional printing layout technology, it provides
unlimited freedom in terms of layout and also features Web capabilities, such as
Internet links.403

As pointed out, standards have been the one underlying force that has been
driving the diffusion of IT, the Internet and the WWW. Being a network
technology in the literal sense, it benefits from network externalities. It is in the
nature of network externalities that “…the utility that a user derives from
consumption of the good increases with the number of other agents consuming the

402 Leiner et al.: A brief history of the Internet, p. 14.
403 See Hobbes: Hobbes' Internet timeline, version 8.1.


Improving Supply Chain Performance

good.”404 In the diffusion process of the Internet, the first widespread standard was
the IBM personal computer platform on the computer level and TCP/IP and the
WWW have been the driving standards behind the Internet diffusion that built the
basis for the possibility of network externality effects.

Besides standardization, the acceptance and diffusion of network technology in
the early 1990s can be attributed to three more trends. These are digitalization,
broadband transmission, and increased performance of distributed computation
power.405 Enhanced by these values, e-business is a direct consequence of the
diffusion of IT, the Internet and the accompanying creation and establishment of
standards. The importance of standards also is treated in the following sections.

3.b. A Business View of E-Business
E-business can be considered an offspring of the development of IT and network
technology, with the Internet being the most significant part. IT received
increasing consideration in the late 1980s/early 1990s through the recognition of
information-based organizations.406 In the mid-1990s, when Internet technology
opened up to the public and began its diffusion, the term e-commerce was
introduced in connection with the first online sales appearances on the Internet.
Over time, the definition has broadened and has started to include not only the sale
of products but also precedent activities. Bloch, Pigneur, and Segev have defined
e-commerce as “the buying and selling of information, products, and services via
computer networks [and the] support for any kind of business transactions over a
digital infrastructure.”407 Watson et al. have seen e-commerce as the usage of IT in
order to extend communication and transaction possibilities with the stakeholders
of an organization. Furthermore, according to Watson et al., these stakeholders
comprise customers, suppliers, administration, financial institutes, managers,
employees and the public in general.408

404 Katz, Michael L. and Carl Shapiro: Network externalities, competition, and

compatibility, in: American Economic Review, Vol. 75 (1985), p. 424.
405 Cf. Fulkerson: Information-based manufacturing in the informational age, p. 134.
406 See for example Drucker, Peter F.: The coming of the new organization, in: Harvard

Business Review, Vol. 66 (1988), January/February, pp. 49-50 and Porter: The

competitive advantage of nations, p. 17 and p. 55.
407 Bloch, Michael, Yves Pigneur and Arie Segev: On the road of electronic commerce - a

business value framework, gaining competitive advantage and some research issues

(No. 1013) 1996, Berkeley, http://groups.haas.berkeley.edu/citm/publications/

papers/wp-1013.html, retrieved on: February 15, 2006, p. 2.
408 Cf. Watson, Richard T. et al.: Electronic commerce: The strategic perspective, Fort

Worth 2000, p. 1.


Holistic View of Supply Chain Management

IBM has coined the term e-business in 1996 through an advertising campaign
resulting in a more differentiated meaning of the terminology.409 In the following
years, e-business and e-commerce have been (too) often considered to be
interchangeable.410 Since then, a more distinguished understanding, however, has
prevailed in which e-commerce represents that part of the much more
comprehensive e-business that embraces the sales-related usage of electronic
media. E-procurement as the supply counterpart represents the procurement-
related part.411

There is some ambiguity among researchers and authors whether or not to
include Internet technology in the general definition of e-business. For example,
Lee and Whang have defined e-business in the context of supply chain integration
as “the planning and execution of the front-end and back-end operations in a
supply chain using the Internet.”412 Chopra and Meindl have noted that “ebusiness
is the execution of business transactions via the Internet.”413
Swaminathan and Tayur remark that “e-business can be loosely defined as a
business process that uses the Internet or other electronic medium as a channel to
complete business transactions.”414 Croom, on the other hand, has defined e-
business “[…] as the use of systems and open communication channels for
information exchange, commercial transactions and knowledge sharing between
organizations.”415

In light of these different interpretations, e-business can be seen as the
electronic support of business processes and relationships with business partners,
employees, customers and other stakeholders.416 This general definition does not

409 Cf. Biggs, Maggie: E-commerce is hot today, but e-business is the gift that keeps on
giving all year long, in: Infoworld, Vol. 20 (1998), No. 21, p. 82.
410 For such a view, see Hoffmann, Christoph: Logistik und Electronic Business,
Wiesbaden 2001, pp. 54-57.

411
This view is shared by numerous authors. For an overview, see Cagliano, Raffaella,
Federico Caniato and Gianluca Spina: E-business strategy: How companies are shaping
their supply chain through the internet, in: International Journal of Operations &
Production Management, Vol. 23 (2003), No. 10, pp. 1143-1144.

412 Lee, Hau L. and Seungjin Whang: E-business and supply chain integration, Stanford
Global Supply Chain Management Forum 2001, p. 2.
413 Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 527.
414 Swaminathan and Tayur: Models for supply chains in e-business, p. 1389.
415 Croom, Simon R.: The impact of e-business on supply chain management, in:
International Journal of Operations & Production Management, Vol. 25 (2005), No. 1,

p. 55.
416
This definition appears with slight variations frequently in the literature. Examples can
be found in Schubert, Petra, Dorian Selz and Patrick Haertsch: Digital erfolgreich,
Fallstudien zu strategischen E-Business-Konzepten, Berlin Heidelberg 2001, p. 14;


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directly relate e-business to any particular technology, especially not to Internet
technology. This understanding emphasizes the fact that e-business had also been
applicable to technologies before the advent of the Internet and the term e-
business itself. More importantly, the definition should be open to new
(electronic) technologies to come. Furthermore, e-business also applies to
technologies that do not actually involve the Internet, especially those that can be
considered to be intra-organizational, such as integrated phone systems, electronic
employee management, digital picture processing, and document management
systems.417

It can be concluded, however, that Internet technology greatly enhances the
possibilities of e-business. Its major advantages are cheap access, common
standards, fast transmission of data, and a widespread digital infrastructure. Kahl
and Berquist have identified the following unique features of the Internet:

(1) ubiquity and pervasiveness, (2) standardization, i.e. common communication
protocol and provision of standardized data transmission, (3) real-time
communication, (4) variety of data structures, which includes product designs,
market data, web site links and production plans, and (5) many-to-many network
configuration, which includes market places, community tools and messenger
tools.418 With further increasing processing power and the improving convenience
of devices, Internet technology drives the “efficient frontier” of e-business
outwards, that is to say it increases product value to customers for the same
process costs or lowers process costs providing the same product value to
customers.419 Standards, transmission technology, devices and applications are
likely to change and to continue developing. E-business developments beyond the
principle capabilities of the underlying Internet technology as pointed out earlier,
however, are hard to imagine at the moment.
The following interview quote by John Bermudez, AMR research analyst,
illustrates fairly well the significance and status of the Internet:

“The next big thing is ‘the real application of the Internet in business. […] I
think it is probably similar to the PC market. […] if you said in 1986 – or
whenever the IBM A.T. was introduced – ‘Oh, the next big thing is the PC,’
people would have responded, ‘But we’ve had PCs for four or five years.

Scheffler, Wolfram et al.: Entwicklungsperspektiven im Electronic Business,
Wiesbaden 2000, p. 5; or Dolmetsch, Ralph: eProcurement, München 2000, p. 27.

417
Besides the “classic” e-business categories, involving businesses, customers and
government as B2B, B2C and so on, also intra-business and non-business as e-business
categories have been included, see Phan, Dien D.: E-business management strategies: A
business-to-business case study, in: Information Systems Management, Vol. 18 (2001),
Fall, pp. 61-69.

418
Cf. Kahl and Berquist: A primer on the internet supply chain, pp. 42-43.

419
Cf. Chopra and Van Mieghem: Which e-business is right for your supply chain?, p. 33.


Holistic View of Supply Chain Management

What’s the big deal?’ The big deal is going from being (something of a
curiosity) to something that supports a bunch of $30 billion companies.”420

Porter has identified five overlapping stages in the evolution of IT and e-
business. The first stage enabled the automation of discrete transactions. In the
second stage, more automation and functional enhancement was achieved, for
example through Computer Aided Design (CAD) and other applications. Cross-
activity integration was added in stage three by linking sales activities with the
order processing process. CIM belongs to this stage and the Internet has already
been involved, although on a rather low level. Currently ongoing is stage four,
where the integration of the value chain and entire value systems is enabled. It
aims at end-to-end applications from the point-of-origin to the point-ofconsumption.
Porter has envisioned a fifth stage, which is thought of integrating
product development and moving Internet procurement from standardized
commodities to engineered items.421 This fifth stage could also be considered as
part of the previous one instead of as a separate entity.

SAP as the leading provider of ERP systems has classified companies with
regard to their SCM IT capabilities along four stages, the “stages of
excellence”:422

-
Stage 1: disconnected systems. These systems focus on automating
existing functions with a low degree of integration.

-
Stage 2: internal and external interfaces. IT systems are still organized
functionally but show a high degree of internal integration. No Web
capability leverage exists and external links are decentralized.
Information exchange happens through e-mail and the Internet.

-
Stage 3: internal integration and limited external integration efficiency.
At this stage, companies are cross-functionally organized. The internal
systems are integrated. Suppliers are linked to the back-end systems and
integrated with buyer front-end system. These systems already show
important supply chain capabilities, such as integrated supply chain
information to plan operations and inter-company process designs.

420 n.a.: Why the Internet is still the 'next big thing', in: Supply Chain Management Review
(2002), May/June, p. 58.
421 Cf. Porter, Michael E.: Strategy and the Internet, in: Harvard Business Review, Vol. 79
(2001), March, p. 74.

422
The four stages of excellence have been incorporated in SAP’s SCM solutions,
currently mySAP SCM as part of their product “Value Calculator.” See also n.a.: Full
Service, in: SAP INFO, No. Issue, May 15, 2002; and their press release on September
19, 2001: “Customers model return on technology investment benefits with free online
tool from SAP”, SAP Press Release.


Improving Supply Chain Performance

-
Stage 4: multi-enterprise integration. Such comprehensive integration
refers to the full application of SCM potential. This includes common
business objectives, seamless information sharing, knowledge
organizations and automated and interactive collaborations. As a result of
end-to-end integration, total visibility into the supply chain network is
available. Trading partners are linked through collaboration and enabled
to operate as one entity.

In conclusion, the full economic impact of IT, the Internet, and e-business
materializes towards the end of their evolution. The latest evolutions, in Porter’s
view stage four and five and according to SAP’s classification stages three and
four, especially underline the importance of e-business for SCM. This constitutes
greatly enhanced capabilities in the areas of coordination, collaboration, and
integration.

3.c. E-Business as a Catalyst for Supply Chain Management
The previous sections have indicated that e-business is the technology that
currently has a significant impact on SCM. E-business can be considered a result
of the shift towards the information age. This is not to say that advances in other
technologies are irrelevant or insignificant. In terms of importance for SCM,
however, e-business is currently the most influential one. As pointed out earlier,
information is of crucial importance for improvements in supply chain
performance. Consequently, the technology that processes information is of
significance.

New technologies and especially e-business capabilities have to be considered
when designing business and supply chain processes. Nevertheless, they should
not drive the design process per se in spite of their seemingly obvious
significance. The business model should lay the foundation for any technology
adoption. Even more so, it is believed that IT and therefore e-business does not
deliver added value in itself, but through the alignment between business strategy
and technology.423

Although this is widely acknowledged, technology-driven implementations are
still considered the area where most IT implementation failures originate. This can
be attributed to the fact that technology overshadows business process
requirements.424 A successful minimalist approach to IT adoption by the Spanish

423 Cf. Park, Sung-Yeon and Gi Woong Yun: The impact of internet-based communication

systems on supply chain management: An application of transaction cost analysis, in:

Journal of Computer Mediated Communication, Vol. 10 (2004), No. 1, November, p. 3.
424 Cf., for example, Field, Alan M.: Think it through, in: The Journal of Commerce

(2005), January 24, 2005, pp. 36-37.


Holistic View of Supply Chain Management

fashion clothing company Zara has been documented by McAfee.425 Based on this
case study, McAfee identified the following guiding principles for the adoption of
IT.426

-
IT as an aid to judgment. As such, it is not seen as a substitute for human
experience and judgment.

-
Standardized and targeted IT adoption. Consequently, only systems and
applications that are seen to provide value are adopted.

-
IT starts from within. Given that, business goals should shape a
company’s use of technology.

-
Processes at the center. IT systems focus on processes and not on
functions.

-
Pervasive alignment. Not only should IT systems be aligned throughout
the organization, but also aligned with the employee’s belief and support
of the IT systems.

These principles have to be carefully applied, though they certainly help to
avoid common mistakes. For example, a too cautious attitude towards the
beneficial nature of IT systems and e-business applications can prevent companies
from adopting them. This might be especially dangerous if certain technologies
emerge as industry standards. It could also impede first mover advantages.
Furthermore, if evaluated separately, certain IT applications might not appear very
beneficial, but if adopted together, their contributions can be much higher than the
sum of its parts.427 Another principle that should be handled with care is the
management of decision support systems. Though this is not supposed to replace
human judgment, it is important to emphasize that human judgment has its own
shortcomings. Thus, decision assistance is of value and should be considered as
such. In line with this, McAfee has explicitly pointed out benefits linked to IT
systems. These comprise, among others, process standardization and deployment;
assurance of compliance with new processes; optimization potentials; automation;
monitoring of processes; comprehensive analyses; control; and reporting. Many of
these processes could not be performed at all or at least not to the degree possible
through e-business systems. In that sense, it can be asserted that e-business has the
potential to reshape coordination, collaboration, and integration between supply
chain partners and to optimize the structure of the supply chain itself.428

425 See McAfee, Andrew: Do you have too much IT? in: MIT Sloan Management Review

(2004), Spring, pp. 18-22.
426 Cf. McAfee: Do you have too much IT?, pp. 20-22.
427 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply

chain: Concepts, strategies, and case studies, p. 286.
428 Cf. McAfee: Do you have too much IT?, p. 22.


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Porter referred to the Internet as being “[…] the most powerful tool available
today for enhancing operational effectiveness.”429 Only if this enhanced and
superior operational effectiveness is sustainable, competitive advantage is
achieved. Standard applications rarely provide such durable advantages. Porter has
argued therefore that strategic positioning becomes even more important: “While
Internet applications have an important influence on the cost and quality of
activities, they are neither the only nor the dominant influence.”430 In line with this
view, Delfmann and Albers have argued that standardized IT and flexibility at low
cost might lead to the consequence that IT can no longer be a competitive
differentiator. In their view, the proportion and importance of physical logistics
compared to the information logistics will increase again.431

Kim and Narasimhan have identified two major points that should be
considered when evaluating e-business activities. The first point is to think beyond
sole information processing capabilities and to recognize the utilization of
technology in order to alter the existing value chain or to engage in a new value
chain. The second point is to consider the capabilities of optimizing structural
connections among supply chain activities. This second point particularly brings
SCM considerations to the forefront.432 This can be summarized as a shift from
viewing IT as an infrastructural support towards understanding IT as a source for
value creation and competitive advantage. This aspect could as well be seen as the
evolution from IT as it was known in the early 1990s to e-business with its
enhanced network characteristics.

Some researchers refer to the Internet when analyzing the potential of e-
business. E-business comprises more than the Internet and these authors indeed
refer to the entirety that is defined here as e-business. Several authors have pointed
out that the Internet itself does not provide supply chain innovation but the
underlying concepts, such as outsourcing, collaboration, or differentiation, have
been available before the Internet emerged. It is undoubted, however, that the

429 Porter: Strategy and the Internet, p. 70.
430 Porter: Strategy and the Internet, p. 75.
431 Cf. Delfmann and Albers: Supply chain management in the global context, p. 68.
432 Cf. Kim, Soo Wook and Ram Narasimhan: Information system utilization in supply


chain integration efforts, in: International Journal of Production Research, Vol. 40

(2002), No. 18, pp. 4587-4588. These two points were identified based on a review of

the views on IT by Earl, M. J.: Management strategies for information technology,

Englewood Cliffs 1989; and Porter, Michael E. and Victor E. Millar: How information

gives you competitive advantage, in: Harvard Business Review, Vol. 63 (1985),

July/August, pp. 149-160.


Holistic View of Supply Chain Management

arrival of the Internet and the accompanying e-business increases the speed of
adoption and the possible scope of these concepts.433

Since each value activity in a supply chain consists of a physical and an
information-processing component, it creates and uses information in different
ways. The impact of IT becomes obvious when linked to the nine categories of
value activities identified by Porter. These are firm infrastructure; human resource
management; technology development; and procurement as supporting activities
and inbound logistics; operations; outbound logistics; marketing and sales; and
after-sales service as primary activities. Before the Internet, IT capabilities have
mainly enhanced each value chain activity independently. Even without the
knowledge of future Internet capabilities, IT alone has already been considered for
creating new linkages and better coordination between these value activities.434 In
2001, Porter has expanded this analysis to the Internet and e-business
capabilities.435

According to Chopra and Meindl, e-business is likely to provide value if any
of the following indications is evident:436

-
The company is exposed to frequent and small sized transactions, uses
predominantly phone and fax, and puts lots of effort in reconciling
product and financial flow.

-
Transactions require limited buyer or seller qualification, there exists a
fragmented and competitive market, and the online site is attractive and
easy to use.

-
The bullwhip effect is significantly present due to information distortion;
low inventory turns; poor product availability; little collaboration for
promotions and new product introductions; and short product life cycles.

The Internet as well as e-business can be seen as enabling technologies,
providing “a powerful set of tools that can be used, wisely or unwisely, in almost
any industry and as part of almost any strategy.”437 Internet and e-business are
considered to be complementary to existing strategies. Additionally, they provide
new opportunities and possibilities for shaping those strategies. The bottom line is
that the Internet as well as IT and e-business are of no economic value in

433 Cf. Sharman, Graham: How the internet is accelerating supply chain trends, in: Supply
Chain Management Review (2002), March/April, p. 19.
434 Cf. Porter and Millar: How information gives you competitive advantage,

pp. 151-153.
435 See Porter: Strategy and the Internet, pp. 63-78.
436 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

pp. 549-550.
437 Porter: Strategy and the Internet, p. 64.


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themelves unless they are put to use in the correct and appropriate context.438
Nevertheless, direct impacts on all nine value activities have been identified. The
conclusion provided by Porter and Millar in 1985 still holds without restrictions:
“The importance of the information revolution is not in dispute: the question is not
whether IT [and e-business] will have a significant impact on a company’s
competitive position; rather the question is when and how the impact will
strike.”439

Amit and Zott have examined the source of value creation possibilities through
e-business. They identified four sources of value creation in e-business that
expand the strategic options of a company:440

-
Efficiency. Includes gains in search costs; selection range; symmetric
information; simplicity; speed; economies of scale; communication costs;
and transaction processing costs.

-
Complementarities. Refers to the fundamental theory that two or more
products can be more valuable together rather than individually. In e-
business, this can occur between products and services for customers,
online and offline assets, technologies and activities.

-
Lock-in. Creates value through e-business by establishing higher
switching costs achieved through loyalty programs, a dominant design,
trust, and high degree of customization. Additionally, positive network
externalities can create value through a lock-in, as for example in the size
of a marketplace such as eBay.

-
Novelty. Refers mainly to first mover advantages and includes new
transaction structures, new transactional content, new participants, and
other novel elements.

Information is of special importance for SCM as it enables many positive
effects in supply chains. Whereas the basic value of information sharing was
analyzed earlier in section B.II.3., e-business and its information systems
determine the infrastructure and technical capabilities to realize the desired
degree, extent, and quality of information sharing.441 To shape these dimensions,

438
Cf. Porter: Strategy and the Internet, p. 65.

439
Porter and Millar: How information gives you competitive advantage, p. 160.

440
Cf. Amit, Raphael and Christoph Zott: Value creation in e-business, in: Strategic
Management Journal, Vol. 22 (2001), No. 6/7, June/July, pp. 503-509.

441
See Li et al.: Comparative analysis on value of information sharing in supply chains, p.

35.

Holistic View of Supply Chain Management

several technologies have been developed and are available.442 Those will be
discussed in the following section.

3.d.
Applications and Developments of E-Business in Supply Chain
Management
In order to employ e-business to shape and enable SCM, the IT systems of
participants have to fulfill fundamental requirements. Without these basic
capabilities it is unlikely that the diverse information systems can ensure a
seamless information, material, and financial flow.

On the IT system level, the IT infrastructure is of crucial importance for the
realization of e-business systems. IT infrastructure consists of components that
build the basis for the collection of data, transactions, system access, and
communication. The following components have been identified by Simchi-Levi,
Kaminsky and Simchi-Levi and represent only the very fundamental ones; more
advanced and recent technologies and applications are subsequently discussed.443

-
Interface and presentation devices. In general, interface devices include
all kinds of devices that make it more efficient to gather and represent
data and information. They also include the usage of Universal Product
Code (UPC), which was introduced in 1973 and is based on barcode
technology. By using barcode scanners, products and processes can easily
be recorded and tracked. The next generation of product identification is
most likely going to be radio frequency identification (RFID), discussed
later in this section.

-
Communication. Fundamental communication modes in the e-business
era are e-mail and formal data exchange by means of EDI.

-
Databases. As the amount of data gathered and stored grows constantly, it
has to be properly organized to retain its value. Gathered data include
transaction information, status information, and general information. It
becomes also increasingly important to comply with legal regulations
when electronic archiving of documents is to replace physical document
archives. E-business applications that involve external members of the
supply chain require advanced, integrated database systems, such as data
warehouses, data marts, and groupware databases.

-
System architecture. The most common internal network architecture is a
client/server system, employing middleware. With increasingly connected

442 What is referred to as technologies here comprises developments in specific standards,
protocols, interfaces, and general computing hardware.
443 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply
chain: Concepts, strategies, and case studies, p. 274-279.


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and homogeneous systems, a special emphasis has to be placed on system
security in terms of fraud, system manipulation, and failures.

Horvath has identified more fundamental attributes for an IT infrastructure
suitable for SCM. An exact definition, however, depends on the specific
circumstances. Nicolai has also named several preconditions for applying e-
business in SCM. These fundamental attributes brought forward by these two
authors include: open standards; low-cost connectivity; large and flexible data
storage capabilities; data maintenance of existing system structures and Enterprise
Application Integration (EAI); systems and channel integration; higher-level self
service capabilities; intelligence gathering and analysis; supply chain collaboration
exchanges; sophisticated security capabilities that ensure safe network structures
(through firewalls, virus scanners, encryption of data and backup systems); and
advanced e-commerce capabilities.444 Additionally, Horvath remarks that the
implementation of new SCM capabilities should become a collaborative process in
itself.445 This underlines the meta-policies on the normative SCM level developed
in this text.

In light of these fundamental requirements towards information systems, it
becomes clear that the integration of applications into ERP systems can be seen as
an important prerequisite to e-business enabled SCM.446 For example, when
adopting an ERP system, typically the need for integrated databases or data
warehouses is realized. The storage and quick retrieval of information is one of the
main characteristics of this system and therefore these capabilities are in place
with a running ERP system. If such data warehouses are transferred to an SCM
system then this can be referred to as business warehouse.447

Ideally, companies possess unique business processes that are mainly correct,
sometimes providing them with a competitive advantage. Then, adopting a
standard ERP system would cause the loss of this competitive advantage and, in

444
Cf. Horvath: Collaboration: The key to value creation in supply chain management, pp.

206-207; and Nicolai, Sascha: eSupply Chain Management als strategisches

Managementkonzept, in: Wannenwetsch, Helmut H. and Sascha Nicolai (Eds.): E


Supply-Chain-Management: Grundlagen, Strategien, Praxisanwendungen,Wiesbaden

2002, p. 11.
445 Cf. Horvath: Collaboration: The key to value creation in supply chain management, pp.

207.
446
See also n.a.: Energizing the supply chain: Trends and issues in supply chain
management, p. 20 and Schreiner, Wilhelm: Entwicklungen und Implementierung von
SCM-Strategien, in: Wannenwetsch, Helmut H. (Ed.): Vernetztes Supply Chain
Management, Berlin Heidelberg New York 2005, p. 381.

447
Cf. Illgner, Elke: Praxisinstrumente für eine erfolgreiche SCM-Realisierung, in:
Wannenwetsch, Helmut H. (Ed.): Vernetztes Supply Chain Management, Berlin
Heidelberg New York 2005, pp. 89-94.


Holistic View of Supply Chain Management

fact, worsen its operations. In such a case it makes sense to customize the ERP
system in a way that fits these unique business processes.448 If this is not done
internally, the company might run the risk that the knowledge about these unique
business processes are communicated through the software vendor. Therefore, it
has to be managed with care.

In themselves, ERP systems have often been seen as a source for providing
additional benefits or competitive advantage. As IT capabilities spread more
widely, however, ERP systems might as well become a necessity for remaining
competitive. ERP systems are not just trivial applications but rather “[…] an
infrastructure that supports the capabilities of all other information tools and
processes utilized by a firm.”449 As such, they decisively build the foundation for
externally integrated ERP systems that therefore support SCM capabilities. This
view is also shared by Chopra and Van Mieghem who posit that “informationprocessing
costs […] tend to be lower for an e-business if it has successfully
integrated systems across the supply chain.”450

In the context of ERP implementation, McAfee has reported an interesting
observation. In a case study analysis conducted immediately after the
implementation of an ERP system, first performance worsened but then improved
over time along a learning curve.451 This phenomenon is also known as the
“worse-before-better” effect and has also been documented by Repenning and
Sterman in the context of quality improvement programs.452 When implementing
an ERP system, the possibility of such performance dips should be considered and
resources should be planned accordingly.

One fundamental function SCM applications have to fulfill is the sharing of
planning and forecasting information in order to improve supply chain
coordination. By doing that, total supply chain costs can be reduced while demand
can be better matched with supply.453 Another crucial objective is to integrate
diverse business systems and applications and through this a seamless information

448 Cf. Bendoly, Elliot and Tobias Schoenherr: ERP system and implementation process

benefits. Implications for B2B e-procurement, in: International Journal of Operations &

Production Management, Vol. 25 (2005), No. 4, p. 307.
449 Bendoly and Schoenherr: ERP system and implementation process benefits.

Implications for B2B e-procurement, p. 306.
450 Chopra and Van Mieghem: Which e-business is right for your supply chain?, p. 35.
451 Cf. McAfee, Andrew: The impact of enterprise information technology adoption on

operational performance: An empirical investigation, in: Production and Operations

Management, Vol. 11 (2002), No. 1, Spring, pp. 40-43.
452 Cf. Repenning, Nelson P. and John D. Sterman: Nobody ever gets credit for fixing

problems that never happened, in: California Management Review, Vol. 43 (2001), No.

4, Summer, p. 73.
453 Cf. Chopra and Van Mieghem: Which e-business is right for your supply chain?, p. 35.


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exchange. As Houlihan already noted in 1985: “Integration, not simply interface,
is the key.”454 The difference between integration and interfaces is that interfaces
are always associated with interruptions. This is not limited to information
systems. When interfaces are present, additional applications or devices are
necessary to transfer information, documents or even physical goods from one
system to the other. As an example, the JIT II concept, where independent supply
chain members share one facility to assemble a final product can be seen as an
integrative physical effort.455 Similarly, independent integrated systems exchange
information as if they were one.

One of the first achieved integrative efficiency gains by IT in inter-company
transactions were realized through EDI. The basic idea of EDI has been to avoid
redundant recording work and thus to ensure a seamless data exchange. To
achieve this, companies have developed interfaces for their systems that have
allowed for the automated exchange of standard business documents. It is
important to differentiate between EDI as the underlying concept of electronic
data interchange in the literal sense and the protocols and formats with which this
is realized. In the very beginning, even the data transmission protocols were
customized, with connections either directly established through the telephone
network or through proprietary, physically-connected wide area networks.
Because of the lack of standard transmission protocols and formats, these first
applications were individual linkages between two business partners and the
relationship-specificity was very high. Subsequently, industry-specific and often
country-specific protocol format standards have been developed, such as EDI for
administration, commerce and transport (EDIFACT) or the standard developed by
the organization for data exchange by teletransmission in Europe for the
automotive industry, ODETTE.456 Though data transmission has also been
transferred to the TCP/IP network protocol and the Internet has been used as the
transmission network, data protocols and formats remained different.
Consequently, these separate EDI legacy systems are generally incompatible with
each other and the installations are rather expensive.457

454 Houlihan, John B.: International supply chain management, in: International Journal of

Physical Distribution & Materials Management, Vol. 15 (1985), No. 1, p. 27.
455 See for example Krajewski and Ritzman: Operations management, p. 495.
456 See Nicolai, Sascha: Praxisinstrumente für eine erfolgreiche eSCM-Realisierung, in:

Wannenwetsch, Helmut H. and Sascha Nicolai (Eds.): E-Supply-Chain-Management:

Grundlagen, Strategien, Praxisanwendungen, Wiesbaden 2002, p. 70.
457 Cf. Evans, Philip and Thomas S. Wurster: Blown to bits, Boston, Massachusetts 2000,

pp. 174-175.


Holistic View of Supply Chain Management

Technical compatibility of physical components and software applications
continues to be a challenge for supply chains.458 There are developments under
way, however, that aim to resolve compatibility problems, and the development of
the Internet and the WWW may serve as a prime example for this process. The
two major categories that drive this development are component technology and
extranet technology. Both categories emphasize the importance of standards.
Developments in component technology are rather technical and IT specific. The
concepts of modularization, encapsulation and plug-and-play component
development, however, are key characteristics that are also of interest for other
areas where integration is of importance, being seen as driving forces for the
development of network technologies.459

In terms of extranet technology, the Internet, based on the TCP/IP network
protocol, is the network technology that links most business partners with each
other. In terms of Internet services, one of the most promising and relevant
emerging data format standards, in particular for SCM, is XML. Tan, Shaw and
Fulkerson see it as “…a set of rules, guidelines and conventions for designing text
formats for structured data so that it is easy to generate and read (by a computer),
can be interpreted unambiguously, extensible, supports
internationalization/localization, and is platform-independent.”460 It does so by
embedding tags in the document that carry structural information and attributes.
These tags make information self-descriptive and indicate the specific meanings of
the information.461 Because of this characteristic, XML documents can be read
independently from a particular application in the way the author intended. XML
has therefore been considered to be the ASCII code of the future.462 Based on the
indicated type, the document can be processed in the specified way after reception,
for example as an order, invoice, complaint or other business document. However,
the further processing of the transmitted information depends on the recipient’s
system. This can be overcome by building a joint, vertical vocabulary that clarifies
such remaining ambiguity.463

This view alone would underrate the versatility of the protocol. Special
specifications exist and can be summarized in four categories: (1) foundation

458 Cf., for example, Siau, Keng and Yuhong Tian: Supply chains integration: architecture

and enabling technologies, in: The Journal of Computer Information Systems, Vol. 44

(2004), No. 3, Spring, p. 70.
459 Cf. Tan, Shaw and Fulkerson: Web-based supply chain management, pp. 43-45.
460 Tan, Shaw and Fulkerson: Web-based supply chain management, p. 46.
461 Cf. Siau and Tian: Supply chains integration: architecture and enabling technologies, p.

70.
462 Cf. Tan, Shaw and Fulkerson: Web-based supply chain management, p. 46. ASCII
stands for American Standard Code for Information Interchange.
463 Cf. Siau and Tian: Supply chains integration: architecture and enabling technologies, p.

70.

Improving Supply Chain Performance

specifications, (2) software infrastructure specifications, (3) semantic
specifications, and (4) application specifications. Though this implies that again
incompatibility is created through these different specifications, the common and
readable underlying format structure using tags remains independent from
specifications. Therefore, even humans would be able to interpret these
documents.464 With these functionalities, XML-based data interchange overcomes
the inherent problems of conventional EDI, namely proprietary standards and
necessary individual customization that cause relatively high implementation
costs, and therefore builds the foundation for a wider adoption in business data
interchange.465

A relatively new protocol to exchange XML messages is SOAP,466 situated
below the network communication protocols TCP/IP and HTTP. These network
protocols were chosen because of their wide acceptance and unproblematic
compatibility with firewalls. SOAP’s main characteristic is that it allows Internet
communication between systems that is independent from platforms and
programming languages.467 The W3C is responsible for the maintenance and
development of the SOAP protocol and does so within its XML working group.468
Because of the relevance of XML and SOAP, these are also at the core of
Microsoft’s “.NET” technology.469

Although standards have been driving the success of the Internet and the
information society, there are several challenges in connection with this. One is
certainly creating and establishing a standard. First of all, high costs are incurred
by the one creating a standard. Once this is established, however, there is a lot of
power connected to the one owning it. With the growing community of open
source developers, it is becoming increasingly difficult to enforce proprietary
standards.

464 Cf. Tan, Shaw and Fulkerson: Web-based supply chain management, pp. 46-47, also
for a more detailed description of the technical aspects only briefly mentioned here.
465 Cf. Gosain, Malhotra and El Sawy: Coordinating for flexibility in e-business supply


chains, p. 32.466 Originally, SOAP was an acronym for Simple Object Access Protocol. However, in its

recent version, this was dropped because of its inaccuracy. Therefore, SOAP is not an

acronym for anything anymore.
467 Cf. Siau and Tian: Supply chains integration: architecture and enabling technologies, p.

71.
468 See World Wide Web Consortium, http://www.w3.org, W3C,retrieved on: February 15,
2006.
469
.NET is a platform developed by Microsoft that comprises Web service servers,
developer tools, applications, and a certified partner organization network to support
the technology. It aims at connecting information, people, systems, and devices through
software, using the Internet. For more information, see http://www.microsoft.com,
category “developer tools”, retrieved on: February 10, 2006.


Holistic View of Supply Chain Management

The long lasting power of Microsoft office document standards can serve as
one example. Microsoft Office document formats became a standard that
prevented many from switching to competing software packages because of the
vast amount of existing files in this format. This might change with the latest
Open Office version 2.0 that not only is able to process existing Microsoft formats
but is also founded on a new, XML-based open standard document format, known
as the “OASIS” format because it has been developed by the Organization for the
Advancement of Structured Information Standards (OASIS). This format is the
result of an international collaboration effort, led by open source developers and
major software companies that joined forces in OASIS. The main advantage of
this new document specification is that it is independent from a specific software
application and is also readable if the respective application does not exist
anymore. Therefore, files can be archived over a long period of time in a machine-
readable way – important for both, private users and companies.470

Increasing standardization also led to the emergence of ERP systems. Even
before ERP systems arrived in the business world, internal integration aspirations
were sought through Computer Integrated Manufacturing (CIM) systems. Then,
IT standards were not advanced enough and the realization of full CIM was
expensive. CIM, however, shared already many basic ideas of current ERP
systems.471

The next step under way is to achieve external information system integration
by means of ERP systems. The term ERP II has been introduced by the Gartner
Group for such extended ERP systems.472 Others refer to such systems simply as
SCM systems.473 Their functions have been expanded to fit the holistic
understanding of SCM. This expansion aims to enable collaborative SCM instead
of company-internal optimization; to include the supplier side, internal operations
and the customer side; to align system processes across the supply chain; to apply
Web-based and open standards; and to shift from internally generated and
consumed information to internally and externally generated and shared
information.474 Extranet technology and the need for integrating business partners
makes it necessary to adapt existing ERP systems, as they were originally

470 Cf. Open Office.org, http://www.openoffice.org, Open Office.org,retrieved on:

February 15, 2006; and Weidemann, Tobias: Open Office 2.0: Das beste Office der

Welt, in: PC-Welt, No. Issue, December, 2005, p. 49.
471 Cf. Kuhn and Hellingrath: Supply Chain Management: Optimierte Zusammenarbeit in

der Wertschöpfungskette, p. 135.
472 Cf. GartnerGroup, Bond, B. et al.: ERP is dead - long live ERP II 2000.
473 For example, cf. Tarn, Michael J., David C. Yen and Marcus Beaumont: Exploring the

rationales for ERP and SCM integration, in: Industrial Management & Data Systems,

Vol. 102 (2002), No. 1, p. 30.
474 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply

chain: Concepts, strategies, and case studies, pp. 272-273.


Improving Supply Chain Performance

designed only for integrating internal functions and processes. Nevertheless,
existing ERP systems and their underlying integrative idea have built the
foundation for upcoming ERP II systems. Therefore, existing ERP vendors like
SAP are best suited to extend these systems to fit these changing requirements
best.475 It can also be concluded that companies that integrate ERP systems well
with their business processes and individual requirements have a solid starting
point to explore possibilities of ERP II or SCM systems.476

Besides advances in software applications, their integration, and network
technologies, physical tools and devices have also been developed to leverage the
foundations laid down. Major relevant areas for SCM are currently mobile
computing power, wireless networking, and new tracking technology.

Mobile computing and wireless networking, often referred to as mobile
business, enhance the capabilities of existing e-business by making it independent
from fixed physical working places and network plugs. The driving technologies
are wireless local area networks (WLAN) and conventional mobile
communication networks, such as the global system for communication (GSM) or
the universal mobile telecommunications system (UMTS). In addition, the global
positioning system (GPS) combined with mobile computing power opens entirely
new navigation related applications. Developments in these areas are likely to
shape and drive e-business and related strategies over the upcoming years by
adding truly ubiquitous network accessibility. Thereby building on previously
developed and established e-business technologies.477

Advances in tracking and tracing technology are of special importance for
SCM. In 1973, barcode technology revolutionized information recording and
processing and is a system still widely used. It works by coding an identification
number in a barcode which can be read automatically by scanners. This
information then is processed through a database that carries relevant information
and returns a certain outcome. In case of a cashier application, it is mainly the
price of a product and a short description. Barcodes are also used in logistics.
FedEx invented online package tracking based on a barcode system. Each package
carries an unique barcode which is recorded at each processing stage. Connected
to a database, which is also connected to the Internet, a package’s status can be
tracked at any time.

475 Bond et al.: ERP is dead - long live ERP II, p. 2.
476 Cf. Tarn, Yen and Beaumont: Exploring the rationales for ERP and SCM integration, p.


33.
477
See also Siau and Tian: Supply chains integration: architecture and enabling
technologies, p. 70; and Illgner, Elke: Grundlagen und Anwendungen der
Internettechnologie im SCM, in: Wannenwetsch, Helmut H. (Ed.): Vernetztes Supply
Chain Management, Berlin Heidelberg New York 2005, p. 44-45.


Holistic View of Supply Chain Management

RFID now is the technology seeking to replace barcode technology. While
barcodes can be printed on almost any kind of package, RFID works with tags, so-
called RFID or simply RF tags, which cab be active or passive.478 Active tags
carry their own power supply and therefore are able to send signals whereas
passive tags carry no power supply and are only activated through a suitable
scanner. Thus, active RF tags can be located over longer distances than passive
tags. RFID technology has several advantages over barcode technology:479

-
Many RF tags can be read simultaneously by a scanner, speeding up the
capturing process.

-
No direct in-sight connection is necessary. Therefore, RF tags can be read
through closed packages or cartons.

-
RF tags can store data and information independently from a database.

With these capabilities go along new functionalities and application
opportunities. For example, warehouse or store inventory can be frequently
recorded in an efficient manner, improving tracking physical items throughout the
supply chain. In a recent study, Thonemann et al. found that retailers and
consumer goods manufacturers see the major impact of RFID over the next five
years in logistics, i.e. transportation and storage processes, and the tracability of
goods.480 A study conducted by the University of Arkansas shows a 16 percent
reduction on out-of-stock products and improvements in speed of shelf
replenishment in Wal-Mart stores that track product cases with RFID compared to
those that are still using barcode technology.481 Also, as RF tag technology
develops, more advanced RF tags that contain integrated circuits can be connected
with other systems such as those which document for example temperature
exposure which could be of importance for frozen food or pharmaceutical
products.

As it has been always one aim that RFID technology would replace barcode
technology, a standard for uniquely identifying items was required. Initially
developed at MIT together with industry leaders and other academic institutions,
the electronic product code (EPC) has evolved and has become the standard for

478
Cf. Prater, Edmund, Gregory V. Frazier and Pedro M. Reyes: Future impacts of RFID
on e-supply chains in grocery retailing, in: Supply Chain Management: An International
Journal, Vol. 10 (2005), No. 2, p. 138.

479 Cf. Sheffi, Yossi: RFID and the innovation cycle, in: The International Journal of
Logistics Management, Vol. 15 (2004), No. 1, p. 1.
480 Cf. Thonemann, Ulrich et al.: Supply chain excellence im Handel, Wiesbaden 2005, pp.
194-196.
481 Cf. n.a.: Report shows how Wal-Mart did it, in: RFID Journal, No. Issue, November 14,
2005, http://www.rfidjournal.com, retrieved on November 18, 2005.


Improving Supply Chain Performance

RF tag identification. Consequently, the EPCglobal Network as the association to
maintain and administrate EPC technology is a joint appointment from EAN
International and the Uniform Code Council, Inc. (UCC), the institutions
responsible for barcode technology standards and in particular the universal
product codes (UPC).482 The major feature of the EPC is the code number itself.
Storing only this information requires the least technical capability on the tag side,

i.e. no integrated circuit is required but only a suitable antenna, thereby keeping
costs low for such applications.483
Essentially, EPC aims to integrate the Internet with RF technology. The
following only briefly describes how EPC based on RFID is supposed to work. A
manufacturer who releases an item into the supply chain defines the information
for the item and stores it in its own EPC information service database. The entry is
reported to the centralized object naming service (ONS), maintained by the
EPCglobal Network. Whenever an item is identified along the supply chain, the
respective readers request item information through a service called EPC
discovery service, which provides the location of the EPC information service that
stores it. The request is then sent to the EPC information service, maintained by
the manufacturer of the item, which in turn provides the requested information.
The structure of the EPC is capable of identifying up to 268 million unique
manufacturers, 16 million product types, and up to 68 billion individual items.
Consequently, the format is capable of identifying hundreds of trillions of unique
items.484

Although major retailers have expressed their intent to introduce RF tags on
their products and RF tags are already used in industrial environments, for a
broader application there are still challenges that have to be resolved. For
example, RF tags are still relatively expensive, though prices are expected to drop
to 5 US cents, which is considered to be the level at which demand is going to soar
significantly. This point should be reached in the not-too-distant future.485
Application on an item level, however, especially for inexepensive consumer
products, is unlikely for the next several years.486

482 EAN stands for the European Article Numbering system.
483 See Heires, Katherine and Ajit Kambil: Tracking RFID's next wave to gain strategic
advantage 2004.

484
Cf. VeriSign, Inc., White Paper, n.a.: The EPCglobal Network: Enhancing the supply
chain 2005; and EPCglobal, n.a.: The EPCglobal Network: Overview of design,
benefits, and security 2004.

485
Cf. Prater, Frazier and Reyes: Future impacts of RFID on e-supply chains in grocery
retailing, p. 138.

486
See Deloitte & Touche USA LLP, Fitzgibbons, Daniel, Lawrence Hutter and Scott
Sopher: Radio frequency identification (RFID): Critical considerations for
manufacturers 2004, p. 3.


Challenges for Supply Chain Management

Other problems are more of technical nature. For example, RF tags have some
interference problems with metallic material and liquids and RF tag scanners have
certain limits in terms of reach. Furthermore, problems exist in the application of
the technology. There is no assigned frequency for RFID and therefore
interference with other applications could occur. Additionally, there exists no
common standard for RFID communication – problematic because of the
importance of standards for a seamless integration and their network effects, as
previously discussed in the context of other standards. There is also the
envisioning of recording the information of all RF tags in one common database,
which then could be accessed through the Internet. In conjunction with this vision,
concerns about privacy, security, necessity, feasibility and desirability have been
raised. Though these problems are indicators for the early development stage of
RFID, few doubt its further application and wide adoption.487 As the major
retailers – Wal-Mart in the USA and Metro in Europe – further expand RFID
applications, the adoption of RFID technology will be inevitably catalyzed.488

E-business in all its varieties will shape and define SCM practices in the
upcoming future. But it also has become clear that technology alone is unlikely to
yield significant benefits without a solid managerial foundation based on SCM
principles. As all core elements of successful SCM have been introduced and
discussed, inherent challenges of the comprehensive nature of SCM as it is
proposed in this text remain to be addressed.

IV. Challenges for Supply Chain Management
1. Involvement in Multiple Supply Chains
Maximizing the monetary overall supply chain profitability with network
members is not an insignificant task, especially when members belong to more
than one supply chain network. In part, this is due to frequently conflicting
objective functions, even within company boundaries.489 This misalignment of
objectives is not necessarily only a policy problem. Often, companies are involved
in more than one supply chain and these rarely share the same objectives and
priorities. Thus, different policies, priorities, and requirements exist, naturally
affecting all supply chains since it is difficult to totally separate them.

487 Cf. Sheffi: RFID and the innovation cycle, p. 2 and p. 9.
488 See Thonemann et al.: Supply chain excellence im Handel, pp. 191-192.
489 See Fleischmann, Bernhard, Herbert Meyr and Michael Wagner: Advanced Planning,

in: Stadtler, Hartmut and Christoph Kilger (Eds.): Supply chain management and

advanced planning: concepts, models, software and case studies (2nd ed.), Berlin

Heidelberg New York 2002, pp. 72-73; Simchi-Levi, Kaminsky and Simchi-Levi:

Designing and managing the supply chain: Concepts, strategies, and case studies, pp. 3


4; or Tan: A framework of supply chain management literature, p. 46.


Improving Supply Chain Performance

A company for example might carry innovative products and functional
products, each requiring its own supply chain configuration.490 Consequently, the
supply chains for each product group should be managed separately. Because this
proves to be a challenging task within an organization, mismatches often arise.491
Table B-5 illustrates possible constellations and generic implications of such
constellations. These constellations also raise additional issues that will be
outlined later.

Table B-5: Complicating matters of multiple supply chains within a company and their
implications

Supplier sells also Implication
Independent products Problem of overhead cost allocation. Maybe
different strategic orientation and competitive
situation of different supply chains.
Same product to competing
supply chain network
Hard to establish a trust-based relationship.
Protective attitude of customer, likely to hold
back information. Hard to achieve advantage
over other supply chain.
Competing products Difficult to share confidential information.
Supplier might tend to benefit the stronger
customer.
Substitute products Difficult to share confidential information.
Supplier likely to shift focus.
Complementary products Little friction. Likely to follow similar
objectives. Still competition for incentive
allocation. Perhaps different supply chain
configuration required.

Even with independent products, such separated supply chains within a
company might affect each other. One problem that can arise is that of cost
allocation, especially if overhead costs are a large portion of the total.
Additionally, the supply chains might be very distinct and require different,
conflicting strategic configurations.

Another problem arises when a company sells the same product to a competing
supply chain network. In this case, establishing a trust-based relationship might

490
See Fisher: What is the right supply chain for your product?, pp. 106-109; and chapter

B.III.1.
491
Cf. Lee, Hau L.: Letters to the editor, in: Harvard Business Review, Vol. 75 (1997),
May/June, p. 191.


Challenges for Supply Chain Management

prove to be very difficult. Downstream supply chain partners are likely to be
reluctant to share sensitive information. In such a constellation, the product under
consideration and the relationship with the supplier is unlikely to support a
competitive advantage over the competing supply chain network.

A similar constellation can be observed with competing and substitute
products. If a supplier also sells competing products – to a competing supply chain
network, obviously – it again is difficult to share confidential information.
Additionally, a supplier might tend to favor the stronger customer or decide to
support one product over the other. With substitute products, this is very likely if
the market potential indicates a switch to the substitute.

Little friction exists with complementary products, as it is very likely that
objectives are not conflicting. Furthermore, by definition, the products could be
considered as sharing the same supply chain or at least their supply chains are
interdependent. A few problems could arise in terms of incentive allocation and
differing supply chain requirements even among complementary products.

The identification of multiple supply chains within a company is important in
order to deal with the implications. An implicit concern in all the above
constellations is that of confidentiality and antagonistic behavior. Partners may try
to achieve competitive advantage over their suppliers or customers in order to
replace them all together or at least to apply pressure on them.492 In such
situations, information may become too visible and companies would be
concerned that other partners take advantage of this. Relevant asymmetric
information can exist in a variety of areas, such as product design, inventory,
costs, demand, and capacity.493 Regarding those costs, “old” thinking states that
the seller is better off the less information she/he shares with her/his buyer. Now
that information is supposed to be shared openly because of its possible, mutually
beneficial effects, it has to be determined how this can be managed successfully
and under what conditions.494

General suggestions to mitigate negative side effects have been proposed by
Liker and Choi. One of them is to share information intensively – albeit
selectively, thus promising improved forecasting, better information visibility, and
timely responses. This way, benefits are grounded in the process, not the
information itself. Selective information sharing refers to risks associated with
information itself. By selecting information and by that taking it to some extent
out of context, its value to others can be limited. Another suggestion is to engage

492 Cf. Knolmayer, Mertens and Zeier: Supply Chain Management Based on SAP Systems,

p. 17.
493 Cf. Swaminathan and Tayur: Models for supply chains in e-business, p. 1388.
494 See Carter, Joseph R. and Bruce G. Ferrin: The impact of transportation costs on supply
chain management, in: Journal of Business Logistics, Vol. 16 (1995), No. 1, pp. 189


190.

Improving Supply Chain Performance

in joint improvement activities and building relation-specific benefits that are
harder to transfer to other relationships.495

2.
Power Regimes in Supply Chains
Besides a collaborative relationship style as mainly proposed here, there obviously
exist also non-cooperative relationship settings that are actually more commonly
adopted based on ”old” thinking. An ignorant attitude towards the possible
benefits of collaboration aside, there are also well-founded reasons in certain
circumstances for adopting transactional relationships.496

Therefore, it is of importance to understand under what circumstances which
relationship designs are beneficial.497 Cox has identified four basic relationship
management choices. These depend on two dimensions: (1) the power condition
of the relationship, or, as Cox calls it, the degree of value appropriation, i.e.
adversarial or non-adversarial, and (2) the relationship style, either arm’s length or
collaborative:498

-
Adversarial arm’s length relationship. This is present when an exchange
partner seeks to maximize its value share and regularly tests the market
for new opportunities.

-
Non-adversarial arm’s length relationship. This is present when an
exchange partner accepts the current market price without overly
bargaining, but still seeks actively for new market opportunities.

-
Adversarial collaborative relationship. This is present when an exchange
partner engages in extensive operational linkages and relationship-
specific adaptations, but still aims to maximize the appropriation of value.

-
Non-adversarial collaborative relationship. This is present when
exchange partners operate as true partners, aiming for a long-term
relationship based on trust and commitment and share any commercial
benefits resulting from this relationship equally.

495
Cf. Liker, Jeffrey K. and Thomas Y. Choi: Building deep supplier relationships, in:
Harvard Business Review, Vol. 82 (2004), No. 12, December, pp. 104-113.

496
Croom found in a recent empirical study that also companies who are aware of the
benefits of supply chain integration recognize that there exist “[…] limits to the extent
to which it is necessary or desirable to integrate the links across the whole supply-side
of the chain.” Croom: The impact of e-business on supply chain management, p. 60.

497
Cf. Swaminathan and Tayur: Models for supply chains in e-business, p. 1392.

498
Cf. Cox, Andrew: The art of the possible: Relationship management in power regimes
and supply chains, in: Supply Chain Management: An International Journal, Vol. 9
(2004), No. 5, p. 353.


Challenges for Supply Chain Management

In order to further refine the definition of the relationship between buyers and
suppliers, Cox developed the power matrix where the following relationships are
identified: buyer dominated relationships; supplier dominated relationships; a
relationship characterized by independence where both parties do not depend on
each other in a significant way and therefore is of rather low importance for both;
and interdependent relationships where the relationship is of high importance for
both.499 In case of buyer or seller dominated relationships, one party dominates the
relationship with superior power over the other.500 Combining the power regimes
and relationship styles, Cox has derived the following Figure B-14.

CollaborativeArm's
LengthRelationship
Relationship

Buyer dominant
arm's length
relationship
Buyer-supplier
independent arm's
length relationship
Supplier dominant
arm's length
relationship
Buyer dominant
collaborative
relationship
Buyer-supplier
interdependent
collaborative
relationship
Supplier dominant
collaborative
relationship

Inequity Equity Inequity

Buyer
Supplier

Power Equality

Dominance
Dominance

Figure B-14: Power regimes and relationship styles501

Whereas an independent relationship between a buyer and a supplier can well
be for low volume purchasing of commodity goods, a perfectly equal,
interdependent power situation is rare, because generally, there is at least a
tendency towards one side being more dominant than the other. The power

499
Cf. Cox: The art of the possible: Relationship management in power regimes and
supply chains, pp. 351-352.

500
Cf. Cox: The art of the possible: Relationship management in power regimes and
supply chains, p. 352 for detailed attributes of each power regime, i.e. power
constellation.

501
Adopted from Cox, Andrew: Business relationships for competitive advantage,
Basingstoke 2004, p. 97.


Improving Supply Chain Performance

regimes identified by Cox can be transferred into an overall supply chain context
by using the formal framework derived in section B.III.2.c.

Instead of comparing only two parties – supplier and buyer – independently,
each participant in a supply chain should be seen in the context of the entire
supply chain. In order to derive the power position of a supply chain member, the
value contribution to the supply chain must be determined. The value contribution
in competitive markets can be measured fairly well by the monetary value added
to the supply chain. The following definitions are used, for the supply chain of
product Q:

[9]: XQt = sQt - vQt

with XQt = total value created in period t by the supply chain network of
product Q that defines the supply chain network
sQt = total sales revenue of product Q in period t
vQt = cost of material and services procured from outside the supply
chain network in period t

The value contribution of a company U to the supply chain of product Q is
defined as:

[10]: XUQt = sUQt - vUQt

with XUQt = total value created in period t by company U for product Q
sUQt = total sales revenue of company U with product Q in period t
vUQt = cost of material and services procured from outside by company

U for product Q in period t

Another variable necessary to fully represent supply chain related power
regimes is company U’s total value creation over all its products P:

plU

[11]: XUt = .
XUPt
P= p1


with XUt = total value created in period t by company U over all products P
XUPt = value created by company U with products P in period t, with
P = p1, … , plU (lU products company U is involved in, Q being
one of lU products)

The reciprocal power relationships can be calculated by relating the above
defined measures to each other to form a supply chain power index and a company
power index, defined as follows:


Challenges for Supply Chain Management

[12]: SC
=
XUQt

powerindex
XUt


with SCpowerindex =
value between 0 and 1; with 0 meaning that company U is
not dependent on the supply chain of product Q, because it
is only a marginal fraction of the company’s total value
creation, and 1 meaning that company U is totally
dependent on the supply chain of product Q, because it is
the only product the company produces.

X

UQt
[13]: Cpowerindex =
X
Qt


with Cpowerindex =
value between 0 and 1; with 0 meaning that the supply
chain for product Q is independent from company U
because company U’s contribution is only marginal, and 1
meaning that the supply chain is totally dependent on
company U, with no other supply chain member involved.
A value of 1 indicates total vertical integration.

In summary, both power indices have a continuous range between 0 and 1.
Values towards 1 indicate a higher power position. A value towards 1 in the
SCpowerindex indicates that a company is more dependent on the supply chain. A
value towards 1 in the Cpowerindex, in contrast, means that the supply chain is
more dependent on a company. Figure B-15 illustrates four resulting, generic
supply chain power regimes, similar to the ones Cox has defined for dyadic power
regimes.502

502 See Cox: The art of the possible: Relationship management in power regimes and
supply chains, p. 352.


130 Improving Supply Chain Performance
highcompany highly
dependent on
supply chain
SCpowerindex
•
13
company defines supply
chain, high vertical
integration, little
product diversification
of company
4
1 2
low
little dependency
for both, company
and supply chain
0
•
company is of high
significance for
supply chain

•
0 •
1
low high

Cpowerindex

Figure B-15: Supply chain power regimes

In the first case – where the company-specific contribution to a supply chain is
low and the supply chain portion of a company’s total value creation is also little –
there is little dependency for both. Such a position is of little importance and it is
likely that the relationship can be characterized as a non-adversarial arm’s length
relationship.

In quadrant 2 and 3 in Figure B-15, either the company or the supply chain is
relatively more dependent on the other. A company’s position in quadrant 2
indicates a strong supply chain position, therefore it is likely that a supply chain
driver role is or can be obtained.503 Then, a company can decide what relationship
it pursues with its supply chain partner, i.e. adversarial or non-adversarial, and
arm’s length or collaborative. In quadrant 3, the supply chain obtains higher
bargaining power and often, an adversarial collaborative or arm’s length
relationship occurs. Ideally, this can be turned into a non-adversarial relationship
with increasing collaborative elements the more important the company’s
contribution to the supply chain is. This relationship should be guided by a
dominant supply chain member or a group of supply chain drivers.

Quadrant 4 depicts the situation of a highly integrated supply chain, where
little value comes from outside the company. A company in such a position
controls the majority of its supply chain. With regard to an increasing focus on

503 See also section B.III.2.


Challenges for Supply Chain Management

core competencies, such a position is not always desirable and a company might
outsource activities and move more towards a quadrant 2 position.

It should be noted that this framework can be extended beyond the purely
quantitative approach, as defined by Equations [12] and [13], and include
qualitative elements. Then, both power indices can be determined through a
weighted scorecard approach, with individual factors defined by a specific supply
chain environment.

The implications of both approaches – the power regimes developed by Cox
and the one derived here – are similar. Whenever one party dominates the other or
others (in the case of the supply chain), the less powerful partner has to be
convinced to engage in collaborative efforts and coordination activities that yield
the highest overall supply chain return. This is especially true for information
sharing as this is a major constitutive source of power in supply chains. The
dominant party is not eager to relinquish its position nor does the less dominant
party want to become even more vulnerable. Clearly, information is not the only
constituent of power in relationships, but as previously discussed an important
one.

Being aware of power regimes in relationships and supply chains is an
significant factor when designing supply chain policies. In order to engage
partners in supply chain cooperation, the issue of incentive allocation is
paramount.

Since SCM is concerned with global supply chain alignment, pareto-efficient
solutions are not desired504 and most likely individual supply chain members
would have to compromise their own profitability in order to ensure such an
overall improved solution. Therefore, a sound benefit allocation should be in place
to obtain the buy-in of those supply chain members that are required to take more
burden than others and thus sacrifice their own profitability. For example, moving
to Vendor Managed Inventory (VMI) adds necessary tasks for the supplier and
therefore requires resources.505 An improvement of overall supply chain efficiency
and effectiveness is assumed but often no benefit allocation is established, thereby
supporting the creation of antagonistic behavior. A questionable example is
provided by Dell, which is proud to collect cash from its customers before actually
paying its suppliers.506 The financing of the goods has not become obsolete, it has
just moved to another supply chain position. Without a more in-depth analysis, the
supplier has to also consider this in the pricing. Such an in-depth cost accounting

504 Cf. Busch and Dangelmaier: Integriertes Supply Chain Management - ein koordina


tionsorientierter Überblick, pp. 12-20.
505 Cf. Subramani, Mani: How do suppliers benefit from information technology use in

supply chain relationships? in: MIS Quarterly, Vol. 28 (2004), No. 1, p. 1388.
506 Cf. Chopra and Meindl: Supply chain management: Strategy, planning, and operation,

p. 19.

Improving Supply Chain Performance

analysis requires not only the sharing of sensitive information, but is further
complicated by the problem of assigning the correct costs to specific supply chains
or relationships. Similar problems are encountered when different products are
coupled.507 Establishing a sound benefit allocation is therefore a challenging task
in supply chains and one that has to be integrated into the overall SCM context.508

3. A Note on Vertical Integration and Uncertainties
A contributing factor to the challenge of overall SCM context consideration is the
implied complexity of a holistic SCM approach. Even a small number of supply
chain combinations can create an almost infinite set of alternatives.509 Operations
research and management sciences deal with such problems, although primarily
with quantitative dimensions. Adding qualitative elements makes this a highly
complex task. Additionally, supply chains are dynamic systems that evolve over
time.510 This is why some executives favor a more integrated supply chain where
more power can be imposed on the individual supply chain positions.511 Indeed,
many of the problems described so far are at least mitigated.512 Such a highly
integrated approach has been documented to have been successfully implemented
by the Spanish clothing company Zara. By not including many external
companies, Zara is able to apply full control over most of its supply chain, having
“…five fingers touching the factory and five touching the customer.”513

However, due to several factors, most often such a high integration is not
feasible.514 Economically, disintegration is based on transaction cost theory. The
underlying theory for such make or buy decisions was first introduced by Coase
and then decisively extended by Williamson.515 Basically it states that if an

507 Cf. Knolmayer, Mertens and Zeier: Supply Chain Management Based on SAP Systems,
pp. 18-19.
508 See for example Delfmann and Albers: Supply chain management in the global context,

pp. 39-40; and Tan: A framework of supply chain management literature, p. 46.
509 Cf. Fleischmann, Meyr and Wagner: Advanced Planning , pp. 72-73.
510 Cf. Simchi-Levi, Kaminsky and Simchi-Levi: Designing and managing the supply

chain: Concepts, strategies, and case studies, pp. 2-3.
511 See Lambert, Cooper and Pagh: Supply chain management: Implementation issues and
research opportunities, p. 3.
512 See Munson, Hu and Rosenblatt: Teaching the costs of uncoordinated supply chains, p.
37, who come to a similar conclusion based on quantitative methods.
513 Cf. Ferdows, Kasra, Michael Lewis and Jose A. D. Machuca: Rapid-fire fulfillment, in:
Harvard Business Review, Vol. 82 (2004), No. 11, November, p. 106.
514 See Williamson, Oliver E.: The economic institutions of capitalism: Firms, markets,
relational contracting, New York 1985, pp. 85-130.
515 See Coase: The nature of the firm, p. 392, and Williamson: The economic institutions
of capitalism: Firms, markets, relational contracting, pp. 15-43.


Challenges for Supply Chain Management

organization can perform an activity better than an external company, it would do
so. According to Coase, a firm reaches a point where an increase in size leads to
diminishing returns because of management inefficiencies, i.e. it becomes more
difficult to control a company as its size increases.516 Again, Williamson has
expanded this view and has also identified disadvantageous factors independently
from firm size.517

Additionally, Williamson has pointed out two main conditions under which a
firm would internalize activities: asset specificity and demand externality. In
general terms, asset specificity, also called more precisely transaction asset
specificity, refers to those assets that are specific to the very purpose of an
activity. However, an external company can posses specific assets that are relevant
for a firm. Transaction asset specificity can be broken down into site specificity,
human asset specificity, physical asset specificity, dedicated assets, brand name
capital, and temporal specificity.518 The second component, demand externality,
refers to aspects that could harm a supply chain upstream company due to the
behavior of companies downstream the supply chain. More broadly, this includes
general environmental uncertainty and behavioral uncertainty.519

If overall acquisition costs are lower than the own production of an activity, a
firm would favor buying such an activity instead of self-production. Acquisition
costs consist of multiple dimensions. According to Clemons, Reddi, and Row, the
total cost of acquisition consists of production costs and transaction costs.
Transaction costs can be broken down into coordination cost, operations risk, and
opportunism risk.520

Coordination cost covers all direct and indirect costs related to the necessary
coordination because of a non-hierarchical relationship. This includes costs related
to various kinds of information exchange and to additional activities necessary to

516 Cf. Coase: The nature of the firm, pp. 394-395.
517 Cf. Williamson, Oliver E.: Markets and hierarchies: Analysis and antitrust implications,
New York 1975, pp. 117-131.

518
Cf. Williamson: Comparative economic organization: The analysis of discrete structural
alternatives, p. 281. This classification is widely accepted, but other classifications
exist, for example see Cousins, Paul D.: The alignment of appropriate firm and supply
strategies for competitive advantage, in: International Journal of Operations &
Production Management, Vol. 25 (2005), No. 5, p. 407.

519
Cf. Williamson: Markets and hierarchies: Analysis and antitrust implications,
pp. 8-10; and Park and Yun: The impact of internet-based communication systems on
supply chain management: An application of transaction cost analysis, p. 4.

520
See Clemons, Eric K., Sashidhar P. Reddi and Michael C. Row: The impact of
information technology on the organization of economic activity: The 'move to the
middle' hypothesis, in: Journal of Management Information Systems, Vol. 10 (1993),
No. 2, pp. 9-35.


Improving Supply Chain Performance

reduce uncertainty or to mitigate its effects. Operations risk includes not only
additional operational uncertainties in terms of on-time delivery and other
increased fulfillment uncertainties, but also uncertainties toward the honesty and
trustworthiness of the other party. It is unrealistic to cover all eventualities that can
occur in a relationship beforehand and therefore differences in interpretation and
commitment are nebulous. Opportunism risk, in contrast, refers to the difference
of bargaining power before and after the engagement in a relationship. Three
sources of opportunism risk have been identified: relationship-specific
investments, small numbers bargaining, and loss of resource control.521 This last
element – loss of resource control – is of especial importance with regard to
technology transfer accompanied by a shift of production to emerging countries.

The idea of transaction cost theory can be directly linked to Porter’s five forces
model. Two of the five forces that define industry attractiveness and profitability
refer to bargaining power,522 the level of which is determined to a large extent by
the elements of transaction cost theory. Low bargaining power not only exists if a
supplier is easy to replace but also if the provided activity can be easily
internalized.

Developments in IT and e-business are believed to reduce coordination costs
and as a consequence to favor market-based relationships. In spite of this, the
number of suppliers has not increased. Bakos and Brynjolfsson have concluded
that this is due to an increased importance of noncontractible investments which
require fewer suppliers in order to provide the necessary investment incentives.523
In a more recent and broader analysis, Park and Yun have confirmed that effects
on exchange mechanisms are not straightforward and depend on multiple effects
on both transaction costs and production costs. Whereas reductions in transaction
costs benefit market systems, reductions in production costs favor hierarchical
systems. In conclusion, they have found no evidence for drastic changes in
existing relationship structures and mechanisms as a result of the developments in
e-business.524

Thus far, the discussion has focused mainly on dyadic relationships. But
relationships within a supply chain network are more subtle. Hammer has
accounted for this by noting that there exist no terms for relationships that go
beyond traditional relationships. For these, the designations of supplier, customer,

521 Cf. Clemons, Reddi and Row: The impact of information technology on the

organization of economic activity: The 'move to the middle' hypothesis, pp. 15-17.
522 See Porter, Michael E.: Competitive strategy, New York 1980, pp.24-29.
523 Cf. Bakos, J. Yannis and Erik Brynjolfsson: Information technology, incentives, and the

optimal number of suppliers, in: Journal of Management Information Systems, Vol. 10

(1993), No. 2, pp. 49-50.
524 Cf. Park and Yun: The impact of internet-based communication systems on supply

chain management: An application of transaction cost analysis, pp. 14-17.


Challenges for Supply Chain Management

and competitor clearly distinguish the role of the respective party. But within a
network, other relevant relationships exist. For example, two companies buy the
same product from the same supplier. (See the second constellation in Table B-5.)
Currently, there exists no appropriate term for this. Another constellation exists
when two suppliers sell different products to the same customer. This constellation
is easier to grasp because less conflict exists. Hammer has defined the relationship
between these two suppliers as cosuppliers. Within such a relationship, synergy
potentials can be sought and used, such as joint transportation or the combining of
non-core activities.525

One concern raised with regard to vertical integration is that benefits gained
from it and/or close cooperation and the accompanying information sharing might
lead to monopolistic power of the whole supply chain, thus leading to the
incitement of anti-trust actions. This is not a new concern. In the 1950s, the US
Supreme Court has looked upon this issue of vertical integration. Spengler has
analyzed the effect and came to the conclusion that only horizontal integration
potentially if at all suppresses competition. Vertical integration as such does not
necessarily suppress competition.526 Hoyt and Huq have come to the same
conclusion. They remark that “[…] a competitive advantage derived from trust-
based, collaborative supply chain alliances will be insufficient to justify anti-trust
actions against the partners.”527

Another challenge for supply chains is that of uncertainty as a complex
network carries many different kinds of uncertainties. Though uncertainty should
always be eliminated as much as possible in supply chains in order to avoid waste,
it is also an inherent part of it. Capacities have to be planned long before actual
demand. For manufacturing capacity planning especially, forecasts are still a
necessity and forecast errors cannot be avoided.528 Inherent uncertainties, besides
the common demand and supply uncertainties, are damages in transportation,
custom procedures, accidents, terror attacks, or natural disasters, among others.
Uncertainties that can be influenced more directly and thus be controlled for to
some degree are those caused by supply chain policies and structures, a lack of
visibility, or lack of cooperation.529

525 Cf. Hammer: The superefficient company, pp. 88-89. See the same source for a broader

discussion of this aspect.
526 Cf. Spengler: Vertical integration and antitrust policy, p. 351, and Williamson: Markets

and hierarchies: Analysis and antitrust implications, pp. 258-259.
527 Hoyt, James and Faizul Huq: From arms-length to collaborative relationships in the

supply chain: an evolutionary process, in: International Journal of Physical Distribution

& Logistics Management, Vol. 30 (2000), No. 9, pp. 760-761.
528 Cf. Fleischmann, Meyr and Wagner: Advanced Planning, pp. 72-73.
529 See Geary, Childerhouse and Towill: Uncertainty and the seamless supply chain, p. 53.


Improving Supply Chain Performance

Geary, Childerhouse, and Towill have identified (1) process uncertainty, (2)
supply uncertainty, (3) demand uncertainty, and (4) control uncertainty as general
types of uncertainty. Process uncertainty involves internal operations; supply
uncertainty refers to uncertainties of supply of upstream supply chain partners;
demand uncertainty refers to uncertainties in ultimate demand or uncertainties due
to wrong demand signaling up the supply chain; and control uncertainty refers to
those posed by algorithms that transfer customer requirements into production
targets and supplier raw material requests.530 Because of market requirements,
companies themselves are sometimes forced to increase demand uncertainties by
introducing a greater variety of products, making forecasting increasingly difficult
as demand can no longer be aggregated. One way to especially counter-balance
such demand uncertainties is the adoption of mass customization and
postponement through modular designs as module demand can be better
aggregated. Thus, the effect of demand uncertainties of individual products can be
tempered.531

Closely related to uncertainty is the issue of risk which can be defined as the
product of uncertainty and impact. Whereas uncertainty refers to the probability of
certain events to happen, impact defines the extent and associated costs of such
events. When supply chains “lean out”, risks are often not adequately considered
because of an inability to realistically grasp uncertainty and therefore risk.532
Consequently, adversarial, non-cooperative relationships are naturally more likely
to disregard risks as competitive prices tend to approach marginal costs. In such
transactions, quotes tend to disregard intangible costs, of which risk can be
considered to be one because of ex ante absence of impact. If impact occurs,
consequences can be severe.

Given a certain service level, uncertainties lead to variations in all operational
metrics and increase costs along the supply chain in form of larger operational
cushions, such as capacities, longer planned lead times, and higher safety stocks.
Consequently, ways to reduce uncertainty are to be sought and the elements of the
SCM framework are capable of dealing with uncertainty issues in the context of
implied cooperative, holistic, and process-oriented attitudes.

530 Cf. Geary, Childerhouse and Towill: Uncertainty and the seamless supply chain, p. 55.
531 Cf. Swaminathan: Enabling customization using standardized operations, p. 127.
532 See Zsidisin, George A., Gary L. Ragatz and Steven A. Melnyk: The dark side of

supply chain management, in: Supply Chain Management Review (2005), March, pp.
46-52. For the definition of risk, see especially p. 48.


C. Supply Chain Management and E-Business in
Manufacturing Companies – a Descriptive Analysis
of Practices
I. Overview of the High Performance Manufacturing (HPM) Project
After laying out the foundations of SCM, it is the aim of this text to empirically
analyze SCM practices in more detail. The analysis is built on the international
research project High Performance Manufacturing (HPM) and data collected in its
second international round in 2004. The HPM research project was firstly
conducted on an international scale in 1996, with the data available in 1997 and
this can be referred to as the first round of the HPM project. At the time, it was
called the World Class Manufacturing (WCM) project, but is now referred to as
the HPM project.533

Since its induction, its main target has been to identify those practices that
determine exceptional performance in manufacturing.534 The underlying HPM
model can be seen as a development and extension of earlier work by Hayes and
Wheelwright and Schonberger and thus more comprehensive.535 The HPM model
identifies six manufacturing practice areas that are seen as determinants of
manufacturing performance:536

- Manufacturing strategy

- Total quality management

533
Before the international rollout, the research framework has been developed to analyze
US owned and Japanese owned manufacturing companies in the USA, see Flynn,
Barbara B., Roger G. Schroeder and Sadao Sakakibara: A framework for quality
management research and an associated measurement instrument, in: Journal of
Operations Management, Vol. 11 (1994), pp. 339-366. For detailed analyses of the first
international round, see Schroeder, Roger G. and Barbara B. Flynn: High performance
manufacturing - global perspectives, New York 2001.

534
See Flynn, Barbara B. et al.: World class manufacturing project - overview and selected
results, in: International Journal of Operations & Production Management, Vol. 17
(1997), No. 7, pp. 671-685.

535
For these contributions, see Hayes, Robert H. and Steven C. Wheelwright: Restoring
our competitive edge - competing through manufacturing, New York 1984 and
Schonberger, Richard J.: World class manufacturing - the lessons of simplicity applied,
New York London 1986.

536
Cf. Schroeder and Flynn: High performance manufacturing: Just another fad?,
pp. 6-9.


Supply Chain Management and E-Business in Manufacturing Companies

- Just-in-time

- Human resources

- Information systems

- Technology management

The underlying procedure of rolling out the survey in the participating plants
remained the same as in 1996. However, based on the experiences of the first
round, the questionnaires have been slightly changed and expanded. Most
noteworthy and especially relevant for the analysis here has been the extension of
the model through scales that are attributed to SCM and e-business. In 2004,
twelve different questionnaires were designed to receive detailed data from the
following members of all hierarchical levels of the participating manufacturing
plants: plant superintendent, plant manager, plant accounting manager, human
resources manager, production control manager, inventory manager, information
systems manager, process engineer, quality manager, member of product
development team, supervisor (three different), and direct labor (ten different).537
If all questionnaires are retrieved and completed, a comprehensive picture of the
plant based on answers from 23 different employees from different hierarchical
levels is obtained. The objective of this approach is to gather information that
covers a multitude of aspects of state-of-the-art manufacturing company
structures. Using many different questionnaires in this way not only assured that
data from different departments and competent persons were collected but also
that information from entire plants was gathered. Comprehensive analyses then
are possible. Besides subjective measurement scales, a variety of objective
measures are obtained from key informants of the plants. This approach
differentiates the HPM approach from many other empirical investigations.538

Subjective measures have been gathered mainly by asking one or more
respondents about previously defined constructs through 7-point Likert scales.
Objective measures have been collected by asking those managers who are
supposed to have this information as part of their functional job assignment. For
example, employee fluctuation was answered by the human resources manager,
defect rates by the quality manager, and general financial information by the plant
accounting manager. This way, key informants for such information were chosen.

537 From the direct labor and supervisor level, the same questionnaire has been answered

by ten, respectively three different employees from this level. However, not all plants

were able to provide always all ten questionnaires from direct labor or three from

supervisors. Four returned questionnaires by direct labor and even only one on the

supervisor level have been accepted as being sufficient.
538 See Flynn et al.: World class manufacturing project -overview and selected results, pp.

671-685.


Overview of the HPM Project

The survey focuses on the following industries: automotive, electronic, and
machinery. These industries have been chosen because they are important sectors
of industrialized production.539 Potential participants have been identified based
on their industry code according to the North American Industry Classification
System (NAICS) or the equivalent systems used in the different countries.540
Additionally, the aim was to approach manufacturing plants from these industries
with more than 100 employees.

In the second round, the number of participating countries also expanded.
Whereas in 1996 five countries participated – the USA, Japan, Italy, Germany,
and the UK – the data collection in 2004 provides current data from six countries:
Finland, the USA, Japan, Germany, Sweden, and South Korea.541 It is important in
such an international data collection to ensure uniformity of the questionnaires in
the different languages and to avoid ambiguity. Since the participating universities
are experts in the field, special terms are transferred properly into the respective
languages. Furthermore, reverse translation has been used to ensure correctness.542

After completion of the data collection, 189 plants participated and were
included in the international database. Six companies have been excluded from
this analysis because of their reported number of employees. Five of them do not
fulfill the requirement of at least 100 employees. One plant is excluded because it
shows a significantly higher number of employees, i.e. 41,589, which is a clear
outlier. Table C-1 provides an overview of the structure of the remaining 183
companies of the survey sample by country and industry.

539
Cf. Devaraj, Sarv, David G. Hollingworth and Roger G. Schroeder: Generic
manufacturing strategies and plant performance, in: Journal of Operations
Management, Vol. 22 (2004), p. 320.

540
In Europe, the respective NACE codes are 29.41, 29.42, 31.1, 31.2, and 34.3.

541
At the time of the analysis in this text, four more countries have been in the process of
collecting data: Italy, the UK, Spain, and Austria. Because the time delay of the data
collection in these countries might pose a problem in terms of comparability, it would
make no sense to wait for completion in these countries.

542
Cf. Devaraj, Hollingworth and Schroeder: Generic manufacturing strategies and plant
performance, p. 320.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-1: Structure of data sample of the second round of the HPM project

Industry
Country
Electronic
Industry
Machinery
Industry
Automotive
Industry Total
Finland 11 6 10 27
Germany 9 13 19 41
Japan 10 11 12 33
South Korea 10 9 11 30
Sweden 7 9 7 23
USA 9 11 9 29
Total 56 59 68 183

30.60% 32.24% 37.16%

The automotive industry represents the largest industry in the sample. The
industry allocation is considered to be satisfactory with 30.60% of respondents
from the electronic industry, 32.24% from the machinery industry, and 37.16%
from the automotive industry.

Furthermore, a more detailed look at the number of employees of the
participating plants is of interest. Table C-2 gives an overview.

Table C-2: Number of employees in the HPM project dataset, according to industry and in
total

Industry
E M A Total
Minimum 156.00 114.00 123.00 114.00
1st Quartile 262.00 203.50 240.75 240.00
Median 486.00 293.00 453.00 414.00
3rd Quartile 1,084.00 536.75 896.50 798.00
Maximum 2,453.00 2,256.00 7,080.00 7,080.00
Mean 730.88 459.40 1,067.80 770.53

Valid Cases 47 48 56 151
Missing Cases 9 11 12 32


Role of Manufacturing Companies in Supply Chains

Of the remaining 183 plants, 32 did not provide information about the number
of employed personnel. It is assumed that those plants fit into the sample because
of the careful identification and selection of companies according to the objectives
of the HPM project.543 In summary, the average manufacturing company in terms
of median value in this sample employs 414 people, with 75% of the plants having
up to 798 employees. Thus, it can be concluded that the sample represents a well
balanced mix of mid-size and large manufacturing plants as they are typical for
the industries under consideration.544

II. Role of Manufacturing Companies in Supply Chains
1. Positions of HPM Plants in Their Supply Chains
In previous chapters it has been highlighted that supply chain networks are
characterized by different power positions. In particular, it has been suggested that
in most cases, one company is a dominant player that holds the main value for a
certain supply chain. This dominant player has been called supply chain driver or
orchestrator. A framework to identify this player has been suggested based on
Cox’ power regimes that have been adapted to supply chains.545 In order to apply
this, however, crucial information about the contributions in terms of added value
of each supply chain member are needed to determine their importance and role
for the supply chain. Though information about the value creation of
manufacturing plants has been collected in the HPM project and will be analyzed
in the next section, this information alone is not sufficient to ultimately derive the
role of these plants in their supply chains.

Another important indicator can be based on an analysis of the sales channels.
Those plants that are closer to the end customer are more likely to possess
additional value of the supply chain because of their direct relationship to the
ultimate demand and thus additional value creating activities. Additionally, more
power and control over ultimate demand can be assumed in case a manufacturer
also maintains close proximity to end customers.546 This is similar to the position

543 Some of these plants provided information about their sales volume. These numbers

also indicate that these plants employ more than 100 employees.
544 According to the U.S. Census Bureau, in 2002 11.24% of manufacturing companies

from the industries considered for this survey had more than 100 employees and these

companies employed 83.68% of all people in these industries, see U.S. Census Bureau,

statistics of U.S. businesses, http://www.census.gov/csd/susb/, U.S. Department of

Commerce,retrieved on: February 15, 2006.
545 See Cox: The art of the possible: Relationship management in power regimes and

supply chains, pp. 346-356, and section B.IV. before.
546 See Kotler, Philip and Kevin Keller: Marketing management, 12th ed., Upper Saddle

River 2006, pp. 467-499 for a general discussion of distribution channels, and Butaney,


Supply Chain Management and E-Business in Manufacturing Companies

of an OEM car manufacturer. The following descriptive analysis is based on this
assumption.

The customer structure of the analyzed plants is used to evaluate the different
positions in the supply chain. Plants were asked to indicate their customer
channels by the given choices (1) end consumer, (2) retailer, (3) wholesaler, (4)
distributor, (5) assembler, and (6) manufacturer. Two-stage-clustering is used to
identify clusters based on their customer structure. In two-stage-clustering, Ward’s
method using the squared euclidean distance measure is used to determine a
suitable number of clusters and cluster means. These are then used as starting
point for the following K-Means clustering method. By combining these two
methods, advantages of both methods can be combined to improve cluster
allocations.547 For the cluster determination, only four customer segments out of
six are considered because the retailer and wholesaler segments show very low
values in all manufacturing plants. Therefore, it is suggested excluding such
variables from the cluster analysis because those variables are rather irrelevant and
likely to influence the cluster allocation negatively.548

Based on the percentage of sales to different customer segments, four clusters
can be identified:

- Cluster 1 (s-to-m), supplier to manufacturer

- Cluster 2 (s-to-a), supplier to assembler

- Cluster 3 (s-to-d), supplier to distributor

- Cluster 4 (s-to-c), supplier to end consumers

The characteristics of each cluster are depicted in Table C-5 below and are
clearly distinct from each other. In order to formally asses this statistically, a
discriminant analysis is conducted, allowing the analysis of whether two or more
groups are significantly different with regard to more than one variable.549 Two
measures are important in order to asses whether differences are significant or not:

Gul and Lawrence H. Wortzel: Distributor power versus manufacturer power: The
customer role, in: Journal of Marketing, Vol. 52 (1988), January,
pp. 52-63 for a more in-depth analysis on channel power.

547
Cf. Backhaus, Klaus et al.: Multivariate Analysemethoden, 11th ed., Berlin Heidelberg
New York 2006, p. 551. The second step – the K-Means clustering – does not
necessarily improve cluster allocations. In this case, the Ward method leads to a “good”
allocation in most conditions, cf. Backhaus et al.: Multivariate Analysemethoden, p.
527-528.

548
Cf. Backhaus et al.: Multivariate Analysemethoden, p. 549.

549
If differences of groups with regards to only one variable are of interest, t-est or
analysis of variance can be used, cf. Backhaus et al.: Multivariate Analysemethoden, p.

156.

Role of Manufacturing Companies in Supply Chains

Wilks’ Lambda and Chi-square. Wilks’ Lambda is determined by dividing
unexplained variance by total variance. Therefore, a direct link to Eigenvalues
exists since Eigenvalues are determined by the ratio of explained variance and
unexplained variance.550 Wilks’ Lambda can be transformed into a probabilistic
variable and so it is possible to draw conclusions with regard to the significance of
dissimilarities between groups. The resulting Chi-square is calculated by the
following formula:551

2 .
J + G .

[14]: X =- N --1 ln .

..
2 ..


with N = number of cases
J = number of variables
G = number of groups
. = Wilks’ Lambda
ln = natural logarithm


From this definition, it becomes also clear that smaller values of Wilks’
Lambda lead to higher significance. In order to evaluate dissimilarity of multiple
groups, univariate Lambdas are multiplied to return a multivariate Wilks’
Lambda.552 This is shown in the following Table C-3 in the first row, function 1
through 3.

Table C-3: Discriminant goodness measures for customer segment clusters

Test of
function(s)
Wilks’
Lambda
Chi-
square
df Sig.
1 through 3 0.002 824.817 12 .000
2 through 3 0.028 489.341 6 .000
3 0.193 225.254 2 .000

Table C-3 shows that all three discriminant functions contribute significantly
to the dissimilarity of the groups. In order to assess whether the results of the
discriminant analysis also possesses predictive power, actual group memberships
as defined by cluster analysis are compared to predicted group membership
through a cross-table. Table C-4 shows the classification results of the

550 Cf. Field, Andy: Discovering statistics using SPSS, 2nd ed., London 2005, p. 592.
551 Backhaus et al.: Multivariate Analysemethoden, p. 183.
552 Cf. Backhaus et al.: Multivariate Analysemethoden, p. 184.


Supply Chain Management and E-Business in Manufacturing Companies

discriminant analysis. In 96.5% of the cases, group prediction is correct and
therefore a high predictive power can be asserted.553

Table C-4: Classification results for customer segment clusters

Original Predicted group membership
Totalgroup 1 2 3 4
1 36 0 1 0 37
2 0 25 0 1 26
3 0 0 24 1 25
4 0 2 0 52 54

It can be concluded that the identified clusters are significantly dissimilar and
the results of a discriminant analysis show high predictive power. Therefore, the
identified clusters are used for further analysis.

As pointed out before, the wholesaler channel and the retailer channel play no
important role for all manufacturers. It is noticeable that 38% of the plants that
answered this question sell predominantly to end consumers. The remaining
manufacturers sell to other manufacturers (26%), assemblers (18%), and
distributors (18%). In terms of procurement channels, those selling mainly to
distributors source most of their materials from other manufacturers and
distributors. The other three clusters purchase mainly from other manufacturers
and raw material suppliers, as depicted in Table C-5.

553 Group allocation by chance would result in only 25% correct predictions.


Role of Manufacturing Companies in Supply Chains

Table C-5: Clusters according to customer segments

Cluster
Percentage of sales to s-to-m s-to-a s-to-d s-to-c
end consumers 0.43% 3.81% 4.88% 79.81%
retailers 3.51% 0.58% 2.67% 5.19%
wholesalers 0.41% 5.08% 10.20% 2.31%
distributors 7.97% 2.85% 73.54% 3.76%
assemblers 1.00% 79.81% 3.20% 5.91%
manufacturers 86.68% 7.88% 4.32% 3.02%
Number of cases 37 26 25 54

Percentage of
procurement from 1 2 3 4
raw material suppliers 34.48% 26.88% 15.79% 25.14%
manufacturers 40.66% 47.98% 45.78% 46.99%
assemblers 7.06% 12.02% 9.00% 15.06%
distributors 15.28% 5.42% 20.71% 7.52%
wholesalers 2.58% 7.70% 8.76% 5.33%

The cluster allocation shows some differences between industries, as
summarized in Table C-6. The automotive industry dominates cluster 2 as
suppliers to assemblers and as such they are generally classified as so-called tier 1
suppliers to OEMs. As many as 21 plants from the automotive industry, however,
also claim to sell predominantly to end consumers. Plants from the electronic
industry sell mainly to manufacturers and end consumers; two very distinct
customer groups. And finally, manufacturers from the machinery industry sell
mainly to what they consider as end consumers, but also to other manufacturers
and distributors. The plants from this industry do not sell to assemblers.

The high ratio of the end consumer segment is surprising for these kinds of
industries. It is very likely that the term end consumer has been understood
differently by the plants responding to the survey. Thus, respondents might
consider their customers as end consumers for their specific products because
maybe there exists no “real” consumer market, but only professional buyers. One
example could be special purpose machines for the public sector or other
companies. In the case of public sectors, these machines are essentially used to
provide services to the public and then in such a way lead to consumption. In the
case of other companies, machines are used in the transformation process of other


Supply Chain Management and E-Business in Manufacturing Companies

products. Only through further processing do they find their way as value to the
customer in a more subtle and indirect manner.

Table C-6: Cluster allocation by industry based on customer segment clusters

 Industry
Cluster
Electronic
Industry
Machinery
Industry
Automotive
Industry Total
s-to-m 14 12 11 37
s-to-a 6 2 18 26
s-to-d 10 12 3 25
s-to-c 15 18 21 54
Total 45 44 53 142

30.82% 30.14% 36.30%

As Table C-5 shows, plants in cluster 4 (s-to-c) also source from rather early
stages in the supply chain. Early stage sourcing indicates high vertical integration
for this cluster because it seems to fulfill also sales channel functions besides
production. Therefore, it can be suspected that those plants have a rather high
vertical supply chain integration. To further examine this, the next section
investigates value creation of manufacturing companies in more detail.

2. Value Creation of Manufacturing Companies
The value creation process is of particular interest in supply chains because it not
only defines the outcome of supply chains in form of products or services but is
also the battlefield for competitive advantage. Added value is an important
measure for evaluating the role and position in a supply chain and an indication
for power regimes in supply chains.554 In the HPM project only limited
information about added value is available. It is almost impossible to obtain all
necessary information in a survey of this scale and scope. An approximation of
total added value per manufacturing plant, however, can be calculated.

In the questionnaires, participants were asked to report sales value of
production and manufacturing costs, allowing an approximation of added value to
be calculated.555 For the analysis, 113 useful responses are available. Conclusions

554 Besides the power associated with the value creation proportion of a supply chain

member, of course practices of the value creation process are important determinants

for competitiveness.
555 An analysis of the numbers provided shows some inconsistencies. For example, profits

based on the financials provided reveal that more than 50% of the respondents would


Role of Manufacturing Companies in Supply Chains

drawn from this calculation have to be used cautiously because of problems in
obtaining correct financial numbers and a high potential of ambiguity. Based on
the information available through the questionnaires, the total added value is
calculated according to the following formula:

[15]: total added value = sales – manufacturing costs * % material costs

This total added value is related to sales value. It can be observed in Table C-7
that plants of cluster 4 (s-to-c) indeed show the highest added value. Since these
plants are also in direct contact with the ultimate end of the supply chain, it can be
concluded that they are likely to have the highest vertical supply chain integration.
Plants that sell predominantly to distributors (s-to-d) show the lowest added value
on the plant level. Thus, it can be suspected that those plants provide less relative
added value to the whole supply chain than others.

Table C-7: Mean and median for total added value per plant for each cluster

Cluster
s-to-m s-to-a s-to-d s-to-c
Mean 57.70% 55.97% 51.47% 61.21%
Median 61.51% 56.38% 44.37% 62.89%
Valid Cases 26 23 16 44
Missing Cases 11 3 9 10

In contrast to total added value, more detailed information is available about
the structure of manufacturing costs alone. Therefore, added value in the
manufacturing operations can be determined more precisely. For this analysis, 131
valid responses are available.556 The added value within the manufacturing cost

have a profit/sales ratio of 19% or higher. Furthermore, 20% report a profit/sales ratio
of more than 35.8%. Therefore, it is very likely that the questions regarding absolute
sales and cost values were ambiguous. In particular, the sales figures might refer to the
sales value of an entire plant as it is stated in the financial statements and not refer to
the actual production value. Additional value added activities, such as sales, marketing,
and research and development activities, would have to be considered as costs as well.
However, only information about manufacturing costs was provided. Consequently,
many costs and activities are likely not to be reflected in these figures. For a detailed
value creation analysis, detailed balance sheets and cost accounting numbers would be
necessary, but are unrealistic to obtain in such a survey.

556
In order to control for irregularities, responses are checked by summing up all three
cost blocks of manufacturing costs, i.e. direct labor, material, and overhead. The sum of


Supply Chain Management and E-Business in Manufacturing Companies

block is calculated by subtracting the portion of material costs from overall costs.
Equation [16] shows the simple formula:

[16]: % of added value in manufacturing = manufacturing costs (100%)

– % of material costs
By only subtracting material costs from manufacturing costs, it is assumed that
overhead costs account for added value, something which could be questioned.
External services and general investment costs would have to be subtracted in
addition to the material costs, whereas personnel and management salaries should
be included. In order to clarify this definition of added value, an explanatory note
seems to be appropriate.

In contrast to many financial interpretations of added value, operational added
value refers to the process of adding value to a product by combining input
factors. Purchased materials and services therefore have to be excluded because
they are the result of the value creation process of another organization. Human
resources, however, are not considered as an input factor of another organization
and their value contribution is specific to the organization where they are
employed and thus they add value to a specific supply chain in the context of an
organization. This context is part of the value creation process because input
factors are transformed into sold products or services and are therefore
included.557

By balancing the major cost blocks of overhead costs as defined above, the
value creation portion outweighs external components and as a result overhead
costs are included as value added activities. Consequently, this calculation tends to
overestimate the value creation of the plants.

Figure C-1 provides an illustration of the distribution of the proportion of
overhead costs in manufacturing costs in the HPM sample. This allows assessing
the possible effect of this overestimation. With a mean value of 21% and a median
value of 16%, the inherent overestimation is rather low.

these should be close to or exactly 100%. If the checksum deviates more than 5%, the
answers are not collectively exhaustive enough and companies are likely to have
misinterpreted the question to an extent that affects valid interpretations of the analysis.
Such cases are excluded from the added value analysis. Overall, more cases are
available than for the financial value creation analysis.

557
For a description of the operational value creation process, see Krajewski and Ritzman:
Operations management, pp. 3-10.


Role of Manufacturing Companies in Supply Chains

Frequency
50
40
30
20
10
0
Std. Dev = 14.45
Mean = 21
N = 131.00
%

Figure C-1: Distribution of overhead cost portion as percentage of manufacturing costs in
the HPM sample

In order to evaluate possible structural differences between the industries of
the sample, the added value distribution within the manufacturing activities of
each industry is analyzed and shown in Table C-8.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-8: Added value in manufacturing function of plants in the HPM project by
industry, in percentage of total manufacturing costs

Industry
E M A Total
Minimum 6.47% 9.00% 9.20% 6.47%
1st Quartile 20.00% 25.50% 20.80% 22.40%
Median 34.00% 38.00% 30.00% 35.70%
3rd Quartile 47.90% 48.50% 46.00% 47.00%
Maximum 79.00% 95.80% 84.00% 95.80%
Mean 35.16% 40.09% 36.51% 37.22%
Valid Cases 45 43 43 131
Missing Cases 11 16 25 52

The values show no great differences between industries and these differences
are not significant.558 The added value ranges from a minimum value of 6.47% to
a maximum of 95.80%. This maximum is almost equivalent to total vertical
integration up to this point in the supply chain. On average, the manufacturing
plants of the sample show an average added value of 37.22%, or 35.70% when
considering the median value.

The added value within the manufacturing function differs substantially
depending on the customer segment being served. Table C-9 shows that those
plants that mainly sell to other manufacturers and assemblers have a higher added
value within their manufacturing function than plants that sell to distributors and
end consumers. An ANOVA analysis shows that the difference in mean value
between the s-to-m cluster and the s-to-d cluster is significant at the p<0.05 level.
This particular result confirms the previous result using the approximation of total
added value, as defined in Equation [16]. For the other clusters, no further
conclusions can be drawn because these results are not significant.

558
The variance is not significantly different between the groups based on a Levine’s test.
Based on a one-way independent ANOVA analysis, there exists no significant
difference of means between the groups, with F(2,128)=0.774, p>0.05. Hochberg’s
GT2 post hoc procedure has been applied as it is suggested as the appropriate test if
sample sizes between groups differ, cf. Field: Discovering statistics using SPSS, p. 341.


Role of Manufacturing Companies in Supply Chains

Table C-9: Added value in manufacturing function of plants according to customer segment
structure

Cluster
s-to-m s-to-a s-to-d s-to-c
Minimum 9.00% 12.00% 6.47% 9.20%
1st Quartile 34.53% 32.08% 15.90% 22.40%
Median 44.00% 43.15% 22.40% 38.00%
3rd Quartile 65.33% 55.35% 42.60% 49.30%
Maximum 98.00% 98.00% 94.00% 95.80%
Mean 48.70% 45.88% 32.85% 40.31%
Valid Cases 30 24 21 51
Missing Cases 7 2 4 3

In order to determine supply chain power regimes, more detailed information
is necessary.559 Unfortunately, neither the supply chain power index nor the
company power index can be calculated because the necessary objective data
about total supply chain value creation are not available. In order to perform the
necessary calculation, a specific supply chain of one product group has to be
defined and separate cost and sales information for this supply chain, both internal
and total supply chain figures, have to be reported.

Although such precise information is not available, plant managers were asked
to evaluate the degree of vertial integration with respect to their total supply chain.
This could be considered an approximation to determine a plant’s supply chain
power position. According to their own judgement, the manufacturing plants in
the HPM sample represent a rather important role in their supply chains. 74.2%
think that they create a medium or high portion of total value of the products they
produce in the hands of the consumers, see Table C-10. Though there are
differences between industries evident, they are not significant.560 According to
this result, 25.8% of manufacturing plants in the sample can be considered to hold
a rather strong position with a high portion of added value in the hands of the end
consumer in their supply chain. Again, it is of interest to compare this perceived
degree of vertical supply chain integration among the previously identified
customer segment clusters. Table C-11 provides an overview.

559 See the definition of supply chain power regimes in section B.IV., pp. 130-136.

560 Based on one-way independent ANOVA analysis, all differences are insignificant with

F(2,156)=0.681, p>0.05. Using Hochberg’s GT2 post hoc procedure, between group

comparisons also show no significant differences.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-10: Perceived vertical supply chain integration of the sample

 Industry
Degree of
perceived vertical
supply chain integration
E M A Total

Very low 4.3% 0.0% 3.3% 2.5%
Low 19.1% 25.5% 24.6% 23.3%
Medium 42.6% 60.8% 42.6% 48.4%
High 34.0% 13.7% 29.5% 25.8%

Valid Cases 47 51 61 159
Missing Cases 9 8 7 24

Table C-11: Perceived vertical supply chain integration by customer segment cluster

 Cluster
Degree of
perceived vertical
supply chain integration
s-to-m s-to-a s-to-d s-to-c

Very low 3.4% 4.3% 0.0% 3.9%
Low 27.6% 21.7% 16.7% 31.4%
Medium 48.3% 52.2% 58.3% 35.3%
High 20.7% 21.7% 25.0% 29.4%

Medium and High combined 69.0% 73.9% 83.3% 64.7%

Valid Cases 29 23 24 51
Missing Cases 8 3 1 3

In contrast to the analysis of added value based on available financial numbers,
plants selling predominantly to distributors believe that they are accountable for
either a medium or high portion of the total value delivered to the end consumer,


Role of Manufacturing Companies in Supply Chains

indicating a relatively strong position in the supply chain.561 A benefit of the
analysis based on subjective assessment over a strictly financial analysis is that the
surveyed plant managers are qualified to also consider intangible factors as
sources of value, such as knowledge and capabilities. A limitation of this
particular measure is that only one informant was asked, although the plant
manager can be considered to be the key informant for this information. In
conclusion, both implications are possible – that plants overstate their own role in
the value creation process or the analysis based on financial information is
erroreneous and that distribution is overall not a major value-creating activity.

Though this information based on this analysis is valuable in order to
underscore the importance of manufacturing plants in the supply chain, the items
in the HPM database are not suitable for drawing more definitive conclusions
regarding the power position of a specific operation in its supply chain.

An area closely related to vertical integration and value creation structure is
that of outsourcing. The concentration on core competencies has led to increasing
importance of outsourcing for companies, especially for non-core competence
activities. Outsourcing refers mainly to the fact that formerly internally performed
activities are transferred to an external partner, who is thought to provide the same
activity more efficiently. In the long-run, it is believed that the effectiveness of an
organization also improves by being more focused. If competitive resources are
affected, it is important to safeguard this in some way, for example through
contracts or copyrights.562

It is of interest to what extent global manufacturers make use of outsourcing
and in what areas, so future research can better define manufacturing supply chain
structures and manufacturing functions in the value creation process. In the HPM
project, six operational manufacturing activities that are considered to be at the
core of manufacturing activities are of interest in terms of their degree of
outsourcing: (1) warehousing, (2) transportation, (3) manufacturing, (4) assembly,

(5) design, and (6) reverse logistics. Additionally, the degree of outsourcing of
administrative, i.e. supportive, activities is asked for. The inventory manager has
been identified as the key informant for these items. All items were rated from one
to five, i.e. from being performed completely internally, predominantly internally,
nearly half and half, predominately outsourced, and totally outsourced.
The analysis of outsourcing activities, as depicted in Table C-12, shows that
only the transportation activity is mainly outsourced. Reverse logistics activities

561 However, the differences between customer segment clusters are insignificant, with

F(3,98)=0.378, p<0.5.
562 See Quinn, James B. and Frederick G. Hilmer: Strategic outsourcing, in: Sloan

Management Review, Vol. 35 (1994), No. 4, Summer, pp. 43-55; and Hagel III, John

and Marc Singer: Unbundling the corporation, in: Harvard Business Review, Vol. 77

(1999), March/April, pp. 133-141.


Supply Chain Management and E-Business in Manufacturing Companies

show an outsourcing level of about 50% on average. All other activities are still
performed either predominately internally or totally internally.563 Most differences
between industries were insignificant. However, a one-way ANOVA analysis
revealed two significant differences of means. First, the comparison of means for
the assembly function shows a significant difference with F(2,151)=5.191, p<0.01.
The electronics industry shows a lower degree of outsourcing in assembly than the
machinery industry with a mean difference of 0.45, p<0.01. This significant but
minimal difference indicates that outsourcing in the electronics industry is of
relatively more importance. Second, the comparison of means for the reverse
logistics function also indicates a significant difference between industries with
F(2,148)=3.872, p<0.05. The automotive industry outsources the reverse logistics
function significantly more than the electronics industry with a mean difference of
0.77, p<0.05. This might be due to historically stricter regulatory rules for the
automotive industry. Table C-12 gives an overall overview of outsourcing in
global manufacturing plants, independent from industry.

Table C-12: Outsourcing of selected activities of manufacturing plants

Warehousing
Transportation
Manufacturing
Assembly Design
Administration
Reverse
logistics
Minimum 1.00 1.00 1.00 1.00 1.00 1.00 1.00
1st Quartile 1.00 4.00 2.00 1.00 1.00 1.00 1.00
Median 2.00 5.00 2.00 2.00 2.00 2.00 2.00
3rd Quartile 2.00 5.00 3.00 2.00 2.00 2.00 4.00
Maximum 5.00 5.00 5.00 4.00 5.00 4.00 5.00
Mean 2.01 4.03 2.28 1.74 1.87 1.63 2.81
Valid Cases 163 162 163 154 158 163 151
Missing Cases 20 21 20 29 25 20 32

Another area of interest, especially in light of the omnipresent discussion of
globalization, is the amount of international purchasing and selling in
manufacturing plants. The data of the HPM project provide evidence that although
purchasing is still mainly conducted through domestic partners as Table C-13
shows, there is a considerable portion of international purchasing visible. Plants in
the electronics industry source significantly more from other countries than the
other two industries. The machinery industry and the automotive industry do not

563
This analysis is not illustrated in detail here because only few implications can be
derived from it and are described in the text.


Role of Manufacturing Companies in Supply Chains

show significant differences and source more from their home country.564
Purchased items in these industries might cause relatively higher transportation
costs and require a higher degree of interaction.

An analysis of sales by countries shows that manufacturing plants also sell
mainly to domestic customers. However, the numbers indicate that sales are more
international than purchases. Table C-14 gives an overview of this. On the sales
side, no significant differences between industries exist. However, the automotive
industry deviates from the other two industries slightly in that they seem to sell
less internationally than the other two. This reflects the tendency of this industry
to locate suppliers around an OEM, which in most cases means suppliers are
located within the same country.565

Table C-13: Percentage of purchases from home country, by industry and in total

Industry
E M A Total
Minimum 10.00% 0.00% 5.00% 0.00%
1st Quartile 30.00% 70.00% 65.75% 51.50%
Median 54.00% 80.00% 80.00% 80.00%
3rd Quartile 81.25% 90.00% 90.00% 90.00%
Maximum 98.00% 100.00% 100.00% 100.00%
Mean 56.08% 75.21% 75.64% 69.28%
Valid Cases 50 49 58 157
Missing Cases 6 10 10 26

564
Levine’s test for equality of variances has been used to determine whether equal
variance can be assumed or not. The null hypothesis of the Levine’s test is that the
variances are homogeneous and therefore are the groups. Equality can be rejected if the
Levine’s test is significant at the p < 0.05 level. In this case, the null hypothesis of
equality of variances is rejected. Therefore, instead of the ANOVA results, robust tests
of equality of means have to be considered. Based on this, geographical purchasing
shows significant differences between industries, with a Welch F-ratio of
F(2,99.44)=9.463, p<0.001. A post hoc analysis shows that plants from the electronics
industry source significantly more from other countries than plants from the machinery
and automotive industries.

565
Based on an ANOVA analysis, differences between groups are not significant with
F(2,151)=2.691, p>0.05, and all multiple comparisons are insignificant based on
Hochberg’s GT2 post hoc procedure.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-14: Percentage of sales to home country, by industry and in total

Industry
E M A Total
Minimum 2.00% 0.00% 0.00% 0.00%
1st Quartile 20.00% 20.00% 42.00% 32.63%
Median 59.50% 50.00% 65.00% 60.00%
3rd Quartile 80.00% 75.00% 90.00% 85.00%
Maximum 100.00% 100.00% 100.00% 100.00%
Mean 53.46% 50.02% 62.96% 56.05%
Valid Cases 48 47 59 154
Missing Cases 8 12 9 29

In conclusion, manufacturing plants still seem to perform most traditional
manufacturing functions internally. As the transportation industry matures, most
manufacturers do not perform transportation themselves but rather rely on external
transportation providers. For geographical sourcing and selling, globalization is a
factor for manufacturing plants, though the analysis shows that average plants
source mainly on domestic markets and that more than half of their sales are on
average to domestic customers.

3. Supply Chain Management Practices and Performance – Empirical Evidence
3.a. Operationalizing Supply Chain Management
To analyze SCM practices, its elements have to be conceptualized based on the
available HPM database. As described earlier, the HPM project collects a variety
of data from each manufacturing plant. The scales have been developed based on
experiences of earlier data collections. Based on the SCM framework previously
developed in this text, the codebook of the HPM database has been screened and
those scale items are identified that are adequate to represent the core model of
SCM, namely SCM cooperation.566 Scale development, therefore, is strictly theory
driven, though based on an existing database.

The elements coordination and collaboration are divided into internal and
external characteristics. Separating internal and external coordination and
collaboration is important because they represent distinct, but nevertheless

566
According to the SCM framework, SCM cooperation consists of coordination,
collaboration, and integration, see section B.I.3c. and Appendix 1.


Role of Manufacturing Companies in Supply Chains

interdependent characteristics. Furthermore, their relationship towards each other
is of interest. From a theoretical viewpoint, one would assume that internal
coordination and collaboration are the basis for their external counterparts. Several
studies have shown that external collaboration plays an intermediary role in that it
is considered a necessary but not sufficient element. Instead, only through its
internal counterpart is it possible to achieve performance gains.567

For internal coordination, five items have been identified. This scale not only
measures the degree to which a plant coordinates activities within its own plant,
but also with other divisions within the same corporation. Respondents have been
asked to assess such kinds of coordination in areas like distribution, planning,
innovation transfer, sales communication, and manufacturing communication.
These five items have been reduced through factor analysis, resulting in one factor
that reflects internal coordination. Four factor loadings are greater than 0.7 and
one item shows a factor loading of greater than 0.6. Cronbach’s alpha is 0.806
with an explained variance of 56.9%. These values satisfy generally suggested
reliability values.568

In contrast, external coordination aims at a plant’s coordination efforts that
reach clearly beyond its own boundaries and considers other supply chain tiers.
Aspects for assessing external coordination cover planning of supply chain
activities; consideration of external forecasts in own planning; total, i.e. holistic,
supply chain consideration; performance tracking of supply chain partners; and
overall monitoring of supply chain performance indicators. Out of the five items
identified, four show factor loadings greater than 0.7 and one carries a value of
greater than 0.6. The Cronbach’s alpha value of 0.810 and explained variance of
57.17% confirm scale reliability.

As with coordination, collaboration has been split up into internal and external
collaboration. Internal collaboration aims at the degree of teamwork-like efforts
within a plant and is measured by whether teamwork is encouraged and
conducted. Items that reflect this measure ask respondents about the usage of

567
Cf. Stank, Keller and Daugherty: Supply chain collaboration and logistical service
performance, pp. 38-39; and Sanders, Nada R. and Robert Premus: Modeling the
relationship between firm IT capability, collaboration, and performance, in: Journal of
Business Logistics, Vol. 26 (2005), No. 1, pp. 14-15. Subrami implies that external
communication is constrained by internal communication processes, cf. Subramani:
How do suppliers benefit from information technology use in supply chain
relationships?, p. 52. This issue will be picked up in more detail later in section D.

568
See for example Homburg, Christian and Hans Baumgartner: Beurteilung von
Kausalmodellen - Bestandsaufnahme und Anwendungsempfehlungen, in: Marketing -
Zeitschrift für Forschung und Praxis, Vol. 17 (1995), No. 3, p. 172. This and all
following scales are documented in Appendix 3, including item reliability and scale
reliability.


Supply Chain Management and E-Business in Manufacturing Companies

teams and small group sessions for problem solving, the impact of such teams in
the improvement process, and the degree of encouragement for independent
problem solving. The factor loadings of all five items are greater than 0.7 and
Cronbach’s alpha is 0.893 with 70.18% variance explained.

External collaboration with customers and suppliers measures collaborative
efforts that reach beyond coordination. External collaboration aims at a more
direct working partnership than coordination. Questions to assess the degree of
external collaboration cover the working relationship with customers and
suppliers, economic attitude towards suppliers, support of suppliers to improve
quality, and communication about quality considerations and design changes.
Three items show factor loadings of greater than 0.8, one is greater than 0.6, and
one item has a rather low factor loading of 0.460. This item is different to the
others because it measures the involvement of customers whereas the other four
aim at supplier relationships. Because this item incorporates a customer side
perspective in collaboration efforts, it is not dropped in order to ensure that
external collaboration with both suppliers and customers is reflected. Overall
reliability is satisfactory with a Cronbach’s alpha of 0.784 and 56.01% variance
explained.

Integration as the third element of the core SCM model has not been covered
appropriately through the items in the HPM questionnaire. Integration represents
the seamless material and information flow along supply chains. Therefore, it can
be seen as an enhancement of the managerial components coordination and
collaboration, which is mainly driven through e-business capabilities. At this
point, only the purely managerial part of the SCM model is analyzed. In section
C.IV., all elements of the core SCM model are considered. In section D, the SCM
framework is conceptualized more in its entirety.

3.b. Determination and Validity of SCM Practice Clusters
The identified measures of SCM practices are the foundation for grouping plants
according to their SCM practice adoption. A factor analysis based on principal
components determined a factor score for each scale and case (plant). Based on
these scores, plants can be clustered according to their relative SCM practice
adoption. Two-stage clustering is applied because the K-Means algorithm has
proved to improve the previously established cluster allocation based on Ward’s
method in so that low and high SCM practice plants are more clearly separated.569

Based on this procedure, four clusters are identified. Differences between
those clusters can be evaluated based on t-values. T-values are norm values. A
positive (negative) t-value indicates that a variable in this particular group is overrepresented
(under-represented) compared to the entire sample. Therefore, it

569 Cf. Backhaus et al.: Multivariate Analysemethoden, pp. 513-514.


Role of Manufacturing Companies in Supply Chains

allows for an interpretation of clusters. T-values are calculated by the following
formula:570

X (J ,G) - X (J )

[17]: t =

S(J )

with (J,G): mean of variable J of observations in group G
(J): mean of variable J of all observations
S (J): standard deviation of variable J over all observations


Table C-15 displays the t-values for all four clusters. The differences between
t-values of the clusters show how they differ and also confirm the clear distinction
between them. According to these t-values, the following four distinct clusters can
be established:

-
Cluster 1, representing low SCM practice. Plants show an overall low
degree of SCM practice adoption.

-
Cluster 2, representing internal SCM orientation. Plants show a relatively
higher degree in internal coordination, are average in external
coordination and internal collaboration, and show below average practices
of external collaboration.

-
Cluster 3, representing external SCM orientation. In contrast to the
previous cluster, plants show a lower degree of internal coordination,
have a slightly higher adoption of external coordination and internal
collaboration practices, but show a much higher degree of external
collaboration.

-
Cluster 4, representing high SCM practice. Plants in this cluster show an
overall high adoption of SCM practices.

Figure C-2 provides an illustration of the four identified clusters, based on
their t-values.

570 Cf. Backhaus et al.: Multivariate Analysemethoden, p. 546.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-15: T-values for SCM clusters

Cluster
low SCM
practice
internal SCM
orientation
external SCM
orientation
high SCM
practice
Internal coordination -1.175 0.365 -0.184 1.177
External coordination -1.104 -0.158 0.383 0.991
Internal collaboration -0.811 -0.186 0.070 1.109
External collaboration -0.812 -0.664 0.829 0.685
Cases 44 48 51 37

% of all cases 24.44% 26.67% 28.33% 20.56%


Cluster 1, low SCM practice
Cluster 2, internal SCM orientation
Cluster 3, external SCM orientation

1.50

Cluster 4, high SCM practice

1.00


0.50

0.00
-0.50
-1.00
-1.50
Internal External Internal External
coordination coordination collaboration collaboration

Figure C-2: Identified SCM clusters of HPM plants, based on t-values


Role of Manufacturing Companies in Supply Chains

F-values provide a criterion to evaluate the homogeneity of groups established
by a cluster analysis. They are calculated as follows:571

V (J ,G)

[18]: F =

V (J )

with V(J,G): variance of variable J in group G
V(J): variance of variable J over all observations


The smaller an F-value is the lower the variance of this variable in a group
(cluster) in comparison to the entire sample. F-values should not be larger than
one. A cluster is considered to be homogenous if all F-values are smaller than
one.572 As depicted in Table C-16, all F-values for the identified four clusters
show values of less than one and are therefore considered to be homogeneous.

Table C-16: F-values for SCM clusters

Cluster
cluster
variables
low SCM
practice
internal SCM
orientation
external SCM
orientation
high SCM
practice
Internal
coordination 0.404 0.248 0.365 0.325
External
coordination 0.332 0.337 0.577 0.594
Internal
collaboration 0.747 0.374 0.741 0.441
External
collaboration 0.603 0.356 0.314 0.503

In addition, a discriminant analysis is conducted in order to evaluate whether
dissimilarities are significant. The following Tables C-17 and C-18 show the
results. All three discriminant functions contribute significantly to the dissimilarity
of the groups. The classification results show 97.2% correctly classified cases and
therefore the predictive power of the outcome is verified.573

571 Cf. Backhaus et al.: Multivariate Analysemethoden, p. 545.
572 Cf. Backhaus et al.: Multivariate Analysemethoden, p. 545.
573 Group allocation by chance would result in only 25% correct predictions.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-17: Discriminant goodness measures for SCM practice clusters

Test of
function(s)
Wilks’
Lambda
Chi-
square
df Sig.
1 through 3 0.097 407.663 12 .000
2 through 3 0.493 123.903 6 .000
3 0.965 6.172 2 .046

Table C-18: Classification results for SCM practice clusters

Original Predicted group membership
Totalgroup 1 2 3 4
1 42 2 0 0 44
2 1 46 0 1 48
3 0 0 51 0 51
4 0 0 1 36 37

It can be concluded that the identified clusters are significantly dissimilar and
the results of discriminant analysis show high predictive power. Therefore, the
identified clusters can be used for further analysis.

3.c. Performance Implications of Supply Chain Management Practices
The previously determined clusters are now used to investigate whether or not
differences in performance can be attributed to the degree of SCM practices
applied in a plant. The following performance measures are considered and
subsequently detailed:

-
Eight subjective, single-item performance measures, assessed by plant
manager.

-
Three objective performance measures, namely on-time delivery, internal
scrap and rework, and returned defective products.

-
Two subjective, multi-item, multi-informant performance measures that
reflect customer satisfaction and distinctive competencies.

The eight subjective, single-item performance measures reflect the assessment
of the plant manager as key informant. The plant manager is a reliable informant
for competitive performance because she/he has all necessary insights to give a


Role of Manufacturing Companies in Supply Chains

reliable assessment.574 She/he was asked to rate competitive performance as
superior, better than average, average, equivalent to competition, or at the low end
of the industry on a global basis. The following eight performance items were
rated: (1) unit cost of manufacturing, (2) conformance to product specifications,

(3) on-time delivery, (4) flexibility to change in product mix and volume,575 (5)
inventory turn, (6) cycle time from raw material reception to delivery of final
products, (7) product capability and performance, and (8) product innovativeness.
For a better comparison of performance, these eight measures are standardized.
Standardized values greater than zero indicate an above average performance
whereas values below zero indicate below average performance.
Three objective performance measures are considered. The first is percentage
of orders shipped on time. Most plants do track their on-time delivery
performance as this is easy to measure and of relatively high importance. It is also
a measure that is independent from general industry characteristics. In competitive
markets, the deliver to promise in terms of delivery date should approach 100% in
all industries, though it can be acknowledged that there might exist slight
differences in terms of relative importance. The same consideration applies for the
performance measure of returned defective products. This second objective
performance item measures the percentage of returned defective products. It
should carry little ambiguity as this can be easily measured. It is also one of
special importance because returned products have high external effects as they
have already been in contact with the customer. They also cause high direct costs
because of the necessary transportation and redundant inbound and outbound
logistics processes within own operations as well as on the customer’s side.

The third ratio used for objective performance comparisons is the percentage
of internal scrap and rework. In this item, varying interpretations are possible.
Some plants might not precisely measure rework or scrap. Others might differ in
their interpretation of what rework and scrap are. For example, the number of
rework incidents along the production process might be enumerated. However,
one product could cause several such incidents and therefore be counted more than
once. Others might measure the cost implications of rework and scrap. Then,
differences might occur due to different cost accounting policies. In conclusion,
though rework and scrap is an important performance item for operations, the way
the question has been raised in the questionnaires might be ambiguous. Despite
these concerns, the ratio is considered for performance comparisons here;

574 About the appropriateness of only one key informant, see Devaraj, Hollingworth and

Schroeder: Generic manufacturing strategies and plant performance, p. 321.
575 Flexibility is measured by two separate items. These two items are combined through

factor analysis, with item reliability of 0.87 for both items, a Cronbach’s alpha of 0.68

and an explained variance of 75.72%.


Supply Chain Management and E-Business in Manufacturing Companies

although it is important that these concerns are considered when interpreting the
results.

Two constructs, represented by designated scales in the HPM database,
represent perceptual performance measures obtained through multi-item scales
that are chosen to compare performance on a broader level. One scale measures
customer satisfaction and the other one evaluates distinctive competencies of the
plants compared to their competition. For both scales, multiple informants in the
plants have been used in order to ensure a high degree of validity of the responses.
It has been suggested that perceptual (subjective) measures can be used instead of
objective measures because they are either not available or, if they are, considered
to be unreliable because of ambiguities. For both measures, no other, more
objective, information is available. The subjective measures, however, are capable
of reflecting the underlying objective reality fairly well.576

Multi-informant responses increase reliability and validity of perceptual
measures, and this is especially important for performance measures.577 Table C19
shows the informants for the scales considered in this study. Note that up to
three supervisors and up to ten direct labor workers returned answers to the items.
Several of these scales have been already introduced in the context of SCM
practices. These are internal and external coordination, internal and external
collaboration, and ERP integration.578 The two constructs trust and customer
orientation will be introduced in section D as they are of relevance for the SEM
analysis.

Customer satisfaction was rated on a 7-point Likert scale by the quality
manager, up to three supervisors, and up to ten production workers. The responses
of the supervisors and production workers are first averaged and then all three
values are averaged again to return the overall plant evaluation for each item.
Overall, five items define the scale. Customer satisfaction is an especially suitable
measure for performance in supply chains because it reflects not only the required
customer focus in SCM but also gives a more embracing picture of a
manufacturing plant’s performance.579 It is also a measure that reflects
performance independently from industry and plant size. To measure distinctive

576 Cf. Dess, Gregory G. and Richard B. Robinson Jr.: Measuring organizational

performance in the absence of objective measures: The case of the privately-held firm

and conglomerate business unit, in: Strategic Management Journal, Vol. 5 (1984), July-

September, pp. 270-271.
577 For a in-depth analysis of validity and reliability of perceptual measures, see Ketokivi,

Mikko A. and Roger G. Schroeder: Perceptual measures of performance: Fact or

fiction? in: Journal of Operations Management, Vol. 22 (2004), especially

p. 262.
578 See section C.II.3.a. before.
579 For a more detailed discussion of customer satisfaction, see section D.II.

Role of Manufacturing Companies in Supply Chains

competencies of the plants compared to their competition, the following six items
were selected: (1) supplier relations, (2) customer relations, (3) enterprise resource
planning, (4) quality improvement programs, (5) SCM, and (6) JIT. Respondents
were the plant manager, the quality manager, and the plant superintendent.

Table C-19: Informants of measurement constructs

Scale Informant(s)
Customer satisfaction Quality manager
Supervisor(s)
Direct labor
Distinctive
competencies
Plant superintendent
Plant manager
Quality manager
Internal collaboration Quality manager
Supervisor(s)
Direct labor
External collaboration Plant manager
Quality manager
Inventory manager
Supervisor(s)
Direct labor
Internal coordination Plant superintendent
Inventory manager
Supervisor(s)
External coordination Plant superintendent
Inventory manager
Supervisor(s)
Trust Plant superintendent
Inventory manager
Supervisor(s)
Customer orientation Quality manager
Supervisor(s)
Direct labor
ERP adoption Information systems manager

Both scales, customer satisfaction and distinctive competencies, are reduced
through factor analysis and the resulting factor scores are used. Consequently,
values greater than zero represent above average performance whereas values


Supply Chain Management and E-Business in Manufacturing Companies

below zero represent below average performance.580 Both scales and the
respective items are reliable. Appendix 3 provides details together with the other
scales.

The objective performance measures have been screened for outliers. Some
plants might have encountered special circumstances in their specific environment
or have submitted erroneous information. By identifying outliers and excluding
them appropriately, these effects can be mitigated. Each of the three measures has
been examined individually. Although this approach is highly subjective, logical
considerations are combined with general considerations for removing outliers.
For example, it has been suggested that outliers can be excluded by defining value
ranges around the mean. Then, only a certain percentage within the range of the
mean are considered.581

For the on-time delivery performance measure, plants that reported a on-time
delivery performance below 70% are considered to be outliers, which represent
the lowest five percent values. In this case, only the worst performing plants have
been excluded. Sometimes, responses from both the top and the bottom of the
range are excluded. In this case, the top five percent cannot be uniquely
identified.582 Additionally, there is little reason to believe that these are erroneous.
In order to control for extreme cases in the scrap and rework performance
measure, both the highest and the lowest five percent of the responses could be
uniquely identified and have been removed. A closer examination of frequencies
of returned defective products raised doubts about six cases because the
percentage was above 50% and appeared unreasonably high. Therefore, these six
plants representing the worst five percent of the responses were removed. Again,
the highest five percent cannot be identified uniquely, but since removals are done
based on the assumption that they have been erroneous, this is not inconsistent.

The identified performance measures are now confronted with the four distinct
SCM practice clusters. They are also used for performance comparisons
throughout the next section. Table C-20 gives an overview of the results. Mean
differences between low SCM practice and high SCM practice are calculated in
the last column and the significance of this difference is indicated.583

580 A Pearson correlation reveals a highly significant positive correlation of 0.346 between

the two scales, with p<0.001. Although this correlation indicates the existence of

interdependence between the two constructs, they are clearly distinct from each other.

By conducting a factor analyis, items of each scale load on two separate factors,

suggesting the distinction of the constructs.
581 Cf. Bortz, Jürgen: Statistik für Sozialwissenschaftler, 4th ed., Berlin Heidelberg New

York 1993, p. 30 and p. 40.
582 13.2% of the respondents reported a 100% on-time delivery rate.
583 The following symbols are used: (*)=p<0.1, *=p<0.05, **=p<0.01, ***=p<0.001.


Role of Manufacturing Companies in Supply Chains

Besides cycle time, all differences in single-item, subjective performance
measures between the low SCM practice group and the high SCM practice group
are significant. The largest difference, 1.007, can be observed with regard to
flexibility. This indicates the special importance of SCM practices for flexibility in
product changes and production volume. Such flexibility is required particularily
in responsive supply chain settings.584 With shorter product life cycles and an
increasingly unstable demand, flexibility skills gain also generally in importance
and SCM practices prove to be especially beneficial for that. For all other
subjective single-item performance measures, the absolute differences in mean
values lie between 0.566 and 0.769.

Differences in objective performance measures are evident, but not on a
significant level. The percentage of on-time delivery is 2.36% higher for plants
with a high SCM practice level compared to the one with a low SCM practice
level. In contrast, internal scrap and rework are 1.22% higher for plants with high
SCM practice adoption compared to the low adopters. A reason for this may be
that plants with a higher commitment towards coordination and collaboration are
better at detecting mistakes throughout the process and also record those mistakes
in a proper manner. Low adopters might have a worse performance but simply are
not aware of it.

This interpretation is supported by the third objective performance measure,
the percentage of returned defective products. Effectiveness with regard to
delivering quality products is best assessed by this performance measure. These
products have been considered internally as being flawless, and in accordance
with quality standards and customer expectations. However, the fact that products
are returned as defective shows not only that this was not the case in the first place
but also impacted customer perception negatively. Here, high SCM practice
adopters receive 1.50% less returns than low adopters. This difference is even
more impressive when considering that this equals a reduction of 55.76%. Plants
that show an external SCM orientation with a high degree of external
collaboration have an even lower return rate of 0.83%.

Customer satisfaction and distinctive competencies as multi-item perceptual
performance measures show large and highly significant differences between low
SCM practice adopters and high SCM practice adopters as well as a gradually
increasing performance for the groups with an internal SCM and external SCM
orientation. The same pattern is evident for distinctive competencies. This gradual
increase is not so evident with all the other performance measures. The difference
of 1.564 in customer satisfaction is remarkable, especially in light of its suitability
to reflect an aggregated view of overall performance. This underscores the
strategic importance of SCM and supports the assumed positive impact of SCM
practices on overall performance.

584 See Fisher: What is the right supply chain for your product?, pp. 105-116.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-20: Performance comparison for different degrees of SCM practices

low SCM
practice
internal SCM
orientation
external SCM
orientation
high SCM
practice
mean diff.
low/high
Manufacturing
costs
Mean -0.390 0.131 0.010 0.268 0.658**
StDev 0.848 0.967 0.978 1.137
Valid cases 41 42 46 34
Conformance to
product
specifications
Mean -0.251 0.043 -0.002 0.328 0.579**
StDev 0.944 0.970 1.084 0.919
Valid cases 42 42 46 34
On-time delivery
Mean -0.463 0.065 0.199 0.217 0.680**
StDev 1.101 0.908 0.983 0.859
Valid cases 42 42 46 34
Flexibility in
product change
and volume
Mean -0.407 0.071 -0.132 0.600 1.007**
StDev 1.009 0.736 1.129 0.841
Valid cases 42 41 45 33
Inventory turn
Mean -0.337 0.068 -0.066 0.432 0.769**
StDev 1.026 0.782 1.086 0.969
Valid cases 42 41 44 34
Cycle time
Mean -0.301 0.135 -0.034 0.265 0.566*
StDev 0.982 0.828 0.957 1.200
Valid cases 42 42 43 34
Product
capability
Mean -0.268 0.007 -0.011 0.368 0.636**
StDev 1.117 0.887 1.050 0.829
Valid cases 42 41 46 34
Innovativeness
Mean -0.356 0.217 -0.118 0.326 0.682**
StDev 1.008 0.941 1.130 0.718
Valid cases 42 41 44 33
On-time
delivery, in %
Mean 92.83% 92.61% 92.65% 95.19% 2.36%
StDev 6.67% 6.90% 7.08% 6.24%
Valid cases 30 37 39 30
Internal scrap
and rework, in %
Mean 5.14% 4.95% 4.46% 6.36% 1.22%
StDev 5.74% 6.37% 6.23% 5.78%
Valid cases 35 31 37 28
Returned
defective
products, in %
Mean 2.69% 1.23% 0.83% 1.20% -1.50%
StDev 5.14% 2.47% 1.35% 2.11%
Valid cases 31 33 41 26
Customer
satisfaction
Mean -0.791 -0.184 0.302 0.773 1.564***
StDev 0.985 0.799 0.758 0.833
Valid cases 44 48 51 37
Distinctive
competences
Mean -0.474 -0.100 0.220 0.341 0.815***
StDev 0.743 0.988 0.973 1.111
Valid cases 44 47 51 37


E-Business Applications and Practices of Manufacturing Companies

After this analysis of SCM practices and their impact on performance, the next
section analyzes e-business applications and practices in manufacturing plants in
detail. This then builds the foundation for the subsequent synthesis of SCM
practices and e-business capabilities of manufacturing plants so that more precise
management implications can be derived.

III. E-Business Applications and Practices of Manufacturing Companies
1. E-Business Usage in Manufacturing Companies
As previously discussed, at the moment e-business represents the most significant
technology for SCM. Though the social impact of the Internet and communication
technology is quite visible, on the manufacturer’s level it is more subtle. Despite
all e-commerce and e-business innovations, in order to sell goods they first have to
be produced and be available. For manufacturers, CIM technology and automation
have been the most pressing technologies to pursue in the past. CIM has now been
integrated into more comprehensive and wide reaching ERP systems.585 Therefore,
the prophecy of integrated manufacturing, planning, and execution systems has
become a reality. Consequently, IT systems are now part of every large
manufacturing plant.

All together, in the HPM project 31 areas for IT application support are
defined. Only 19 of these are seen to be of immediate relevance for SCM.586 The
plants were asked whether a function is supported by software and if so, if it is
integrated in an ERP system. A comparison of the six countries shows that there
exist different degrees of SCM software support and especially ERP integration.
German plants, for example, show the highest degree of software support for SCM
application areas, closely followed by plants located in the USA and Japanese
plants as shown in Table C-21. The low software support rate of South Korean
plants can be accounted for by the economic environment of this country. Lower
relative wages may lead to a higher degree of activities done manually.587

585 CIM technology has been further developed. SAP, for example, refers to its solution

that covers product development and production processes as Product Lifecycle

Management, see http://www.sap.com.
586 See Appendix 2 for the list of all application areas considered to be relevant for SCM.
587 For South Korea, in 2005 labor productivity in US$ per hour has been on average 50%

lower than for the other countries in the database. However, since 1990 it has been

improved by 100% compared to around 30% in the other countries. This shows the

economic development of South Korea of the last years. See Total economy database,

http://www.ggdc.net, The Conference Board and Groningen Growth and Development

Centre,retrieved on: February 15, 2006.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-21: Software support of selected SCM applications by country

Software support of selected
applications
ERP integration of selected
applications
Country Rank among
countries Average % Rank among
countries Average %
Germany 1 77.7% 1 53.9%
USA 2 77.2% 3 40.9%
Japan 3 74.3% 6 23.0%
Sweden 4 68.1% 4 38.0%
Finland 5 63.4% 2 41.3%
South Korea 6 57.3% 5 28.0%

ERP integration presents a different picture. German plants still show the
highest degree of ERP integration, followed by Finland, the USA, and Sweden.
Though Japan ranks third in terms of SCM software support, when it comes to
ERP integration, it drops to last by quite some margin. Japanese manufacturing
plants seem to see no necessity of extensive ERP integration of their software
applications and rather work with more isolated systems. This is illustrated in
Figure C-3.


E-Business Applications and Practices of Manufacturing Companies

80.0%

80.0%

70.0%

70.0%

60.0%

60.0%

50.0%

50.0%

40.0%

40.0%

30.0%

30.0%

20.0%

20.0%

Figure C-3: Illustration of SCM software support and ERP integration588

This overall picture differs substantially in two application areas. Though
Japan and South Korea show overall a low degree of ERP integration, in the
application and ERP integration of groupware tools they show higher percentages
than the other countries. 96.3% of all Japanese plants in the sample use groupware
tools and 18.5% integrate them into their ERP systems. With 78.6%, South
Korean plants show an unusually high software support of groupware tools and
with 14.3% in this application the second highest ERP integration after Japan.
This indicates that Asian plants see a higher need for software support in group
activities relative to the other countries and therefore it can be suspected that they
emphasize these kinds of activities over other areas. Besides this deviation in the
application of groupware tools, 85.2% of the Japanese manufacturing plants
support their product configuration by software applications and 33.3% integrate
this function into their ERP systems. This software support is again the highest
among all countries.

As IT becomes increasingly important for operations, the associated costs are
of interest. Often, this is used as a benchmark of a company’s IT activity and
proficiency. Figure C-4 shows the median values and range of IT expenses of the
analyzed manufacturing plants as a percentage of manufacturing costs by country.
The median is favored over the mean because the sample shows high standard
deviations and therefore outliers affect the mean too much. With a median of 3.5%
of overall manufacturing costs, South Korean plants show the highest average IT
cost proportion, followed by Japanese plants with a value of 3% and German
plants with 2.9%. In comparison to other cost components, IT expenses might be

588
The bar chart refers to the percentage of software support and the line chart within
refers to the percentage of ERP integration.

% of
software
support
forselected
applications

GER USA JPN SWE FIN KOR


%
of
ERP
integration
of
selected
applications


Supply Chain Management and E-Business in Manufacturing Companies

relatively higher for South Korean plants as facilities and labor costs are relatively
lower compared to the other countries participating in the HPM study, thus
leading to a higher cost proportion in manufacturing costs. Considering the high
degree of IT adoption in German plants, it comes as no surprise that these plants
spend slightly more on IT than other countries in a comparable economic
environment.

Both, South Korea and Japan, with a relativly low ERP integration rate show
higher median (and also mean) IT expenses of manufacturing costs than the other
countries. This suggests that a less integrated approach tends to cause higher
overall IT costs. German plants, however, show the third highest IT expense ratio
although still being the most integrated ones. Nevertheless, a closer look at all
three measures combined – software support, ERP integration, and IT expenses –
may provide an explanation. It can be observed that IT productivity – measured in
general by the ratio of output to input – might well be best for German plants
because of the disproportionally higher service offering. Under this assumption,
the conclusion that a less integrated approach tends to cause relatively higher IT
costs holds.589

0
5
10
15
20
%
of
manufacturing costs
1.7%
2.7%
3.5%
2.5% 2.9% 3.0%
min
max median of IT expenses
KOR JPN GER USA SWE FIN

Figure C-4: Reported median of IT expenses and range as percentage of manufacturing
costs by country

589 It should be noted that these numbers are based on a relatively low overall response rate
of 112 plants.


E-Business Applications and Practices of Manufacturing Companies

Besides software support and their internal integration, the Internet has
provided a variety of new possible service offerings for suppliers and customers of
manufacturing plants. Several possible uses of the Internet in terms of
procurement and sales activities have been of interest in the HPM project. In terms
of procurement, the Internet is mostly used for scanning the market for new
sources. Also, 57.9% of manufacturing plants use the Internet for transmitting
orders to suppliers and 39.8% use it for tracking and tracing orders. Table C-22
gives an overview. Bold figures indicate the highest value for each Internet
activity. So far, it seems that there is little use of the Internet as a platform for
collaborative activities, real-time integration, and dynamic pricing. Either
manufacturers do not see a benefit in these activities or their supply chains are not
responsive to these activities. With all the possibilities of the Internet, this
indicates either that the real application of the Internet is still in an early stage or
that these functions are not perceived as beneficial at the moment or both.590

Table C-22: Usage of the Internet for procurement activities by country and in total

pricing
transmittingordersto
suppliers
potentialnewsources
receivingand
comparingsupplier
offers
providedynamic
scanningmarketfor
trackingandtracing
supportcoll.
product
designandimpr.
supportcoll.
process
designandimpr.
orders
real-timeintegration
FIN 40.7% 11.1% 14.8% 55.6% 29.6% 14.8% 22.2% 0.0%
USA 78.6% 57.7% 28.0% 76.9% 82.1% 34.6% 16.7% 20.0%
JPN 75.0% 37.5% 29.2% 75.0% 33.3% 20.8% 12.5% 4.2%
GER 85.0% 47.5% 20.0% 52.5% 25.0% 5.0% 15.0% 10.0%
SWE 89.5% 36.8% 10.5% 57.9% 57.9% 5.3% 5.3% 10.5%
KOR 53.6% 28.6% 32.1% 35.7% 21.4% 14.3% 17.9% 21.4%
Total 70.5% 37.2% 22.7% 57.9% 39.8% 15.2% 15.4% 11.0%

Using the Internet on the sales side of the plants is dominated by two areas:
presenting information, and providing a sales product catalog. Online order entry
and checking delivery status online for business partners follow by considerable
margins. Table C-23 provides an overview.

590
One limitation of these items is certainly that the wording is rather vague and therefore
it might have been unclear to the respondents what exactly was meant by these Internet
activities.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-23: Usage of the Internet for sales activities by country and in total

offers
dynamicpricing
presentinginformation
presentingsales
productcatalogonlineproduct
configuration
fixedpricing
checkdelivery
statusonline
offers
onlineorderentry
FIN 88.9% 40.7% 11.1% 3.7% 3.7% 11.1% 11.1%
USA 90.0% 42.1% 12.5% 23.5% 18.8% 41.2% 23.5%
JPN 77.3% 77.3% 9.1% 13.6% 9.1% 31.8% 9.1%
GER 87.8% 72.5% 17.5% 15.0% 12.5% 20.0% 22.5%
SWE 89.5% 63.2% 31.6% 21.1% 0.0% 36.8% 31.6%
KOR 60.7% 64.3% 21.4% 14.3% 0.0% 17.9% 21.4%
Total 82.2% 61.3% 17.1% 14.4% 7.2% 24.2% 19.6%

The usage of the Internet according to customer segments provides additional
insights into how Internet services are used by manufacturing plants involved in
different sales channels. It has been suggested that the Internet as a procurement,
marketing, and sales channel should be designed and used according to customer
needs.591 The analysis depicted in Table C-24 shows that plants that primarily sell
to distributors use the Internet the most for their sales activities. Only plants
selling mainly to end consumers have a higher Internet usage in presenting
information on the Internet. Bold figures indicate the highest value for each
Internet activity.

Table C-24: Usage of the Internet for sales activities by cluster and over all clusters

fixedpricing
onlineproduct
configuration
presentinginformation
presentingsales
productcatalog
dynamicpricing
onlineorderentry
checkdelivery
statusonline
s-to-m 79.2% 50.0% 9.5% 13.6% 4.8% 27.3% 18.2%
s-to-a 79.2% 60.9% 13.0% 13.0% 0.0% 17.4% 8.7%
s-to-d 73.9% 69.6% 30.4% 26.1% 13.0% 39.1% 26.1%
s-to-c 88.5% 63.5% 13.5% 11.5% 9.6% 19.2% 17.3%
Cluster

Total 82.1% 61.5% 16.0% 15.0% 7.6% 24.2% 17.5%

591 See Fritz, Wolfgang: Internet-Marketing und Electronic Commerce, 3rd ed., Wiesbaden
2004, pp. 131-140.


E-Business Applications and Practices of Manufacturing Companies

For sales activities, mainly generic functionalities are adopted, as is the case
for presenting information or presenting a sales product catalog. Order entries
through a webpage are only adopted by 24.2% of the plants. However, this
function is most likely fulfilled through a more integrated approach, such as the
automatic exchange of standard information through EDI. Unfortunately, these
areas are not covered in more detail and consequently this cannot be answered
definitively.

Besides sales activities over the Internet, procurement activities using the
Internet by each cluster are also analyzed, as depicted in Table C-25. Plants selling
predominately to distributors show a relatively intensive usage of the Internet.
They use the Internet more than the other clusters in receiving and comparing
supplier offers, providing dynamic pricing, transmitting orders to suppliers, and
supporting collaborative product design and improvement. Plants selling mainly to
manufacturers use the Internet intensively in scanning the market for potential
new sources, tracking and tracing orders, real-time integration, and support of
collaborative process design and improvement.

Table C-25: Usage of the Internet for procurement activities by cluster and over all clusters

pricing
transmittingordersto
suppliers
potentialnewsources
receivingand
comparingsupplier
offers
providedynamic
scanningmarketfor
trackingandtracing
supportcoll.
product
designandimpr.
supportcoll.
process
designandimpr.
orders
real-timeintegration
s-to-m 86.2% 35.7% 18.5% 59.3% 51.7% 22.2% 7.7% 19.2%
s-to-a 77.3% 36.4% 22.7% 54.5% 40.9% 18.2% 13.6% 4.5%
s-to-d 54.2% 45.8% 41.7% 75.0% 37.5% 20.8% 33.3% 16.7%
s-to-c 81.5% 44.4% 18.5% 53.7% 38.9% 7.4% 13.0% 9.3%
Cluster

Total 76.7% 41.4% 23.6% 59.1% 41.9% 15.0% 15.9% 11.9%

It can be concluded that manufacturing plants make use of the Internet and the
WWW more intensively for procurement activities than for sales activities.
Besides the more generic usage for scanning the market for potential new sources
and comparing supplier offers, more operational functions are of importance, such
as the transmission of orders to suppliers, or tracking and tracing orders. For this,
the Internet represents not only a cheap communication channel with advanced
self service functions but also contributes potentially to a lower data recording
error rate. More advanced, but also admittably less developed functionalities, such
as real-time integration or support for collaborative processes, are not very
widespread among manufacturers yet. It seems as if so far these functionalities are
not capable of replacing the existing processes efficiently.


Supply Chain Management and E-Business in Manufacturing Companies

2. Electronic Integration of Business Partners in Manufacturing Supply Chains
Information and especially the sharing of it is of high importance for SCM. In
order to create a seamless supply chain, information sharing processes should also
be integrated and automated. Ideally, ERP systems of business partners exchange
information in a structured way so they can be processed without manual
interference.

The most common way to conduct such a structured information exchange is
through EDI. In the HPM project, 24.7% of the manufacturing plants exchange
structured information with either their customers or their suppliers; in addition,
49.4% out of 166 responses recorded in this particular item report doing so with
both. This is a quite high adoption rate. Independently from this, plants were asked
about their usage of the Internet in the purchasing and sales processes. With this
question, the degree of Internet-based supply chain integration can be determined
similar to the framework suggested by Frohlich and Westbrook.592 However, a
rather low adoption rate becomes evident. Most companies report that they make
only little use of the Internet in their purchasing and sales processes. This is in
contrast to the previous outcome, because the exchange of structured information
should be also part of purchasing and sales processes and one could assume that
this exchange is conducted over the Internet.

This contradiction raises questions about the validity of responses. One
explanation might be that the respondents misinterpreted the questions. Plants
have not been explained what exactly is meant by structured information or for
what purposes or to what extent they actually exchange structured information. It
might be that it is not the case for major supply chain processes, such as
processing purchases or sales. Additionally, it is possible that respondents failed to
make the connection between EDI system exchange and the Internet due to
different perceptions of the Internet, i.e. interpreting it as the WWW.

Bearing this in mind, it is of interest whether plants with a high Internet
adoption rate perform better than their low adoption counter parts. In order to
identify plants with a high Internet adoption for their purchasing and sales
processes, a cluster analysis using Ward’s method was conducted. Five
homogeneous clusters were identified based on the usage of the Internet for
purchasing and sales processes. Figure C-5 provides an overview and Table C-26
shows the mean values for these clusters.593

592 See Frohlich and Westbrook: Demand chain management in manufacturing and

services: Web-based integration, drivers and performance, p. 731.
593 For this analysis, a two-stage-clustering analysis did not improve the results. In the

second stage, only four clusters were identified. By reviewing the cluster means, a five

cluster allocation seems to be the better solution because they show five distinct

positions with regard to the usage of the Internet for purchasing and sales activities.


E-Business Applications and Practices of Manufacturing Companies


Cluster 1, no Internet
Cluster 2, little Internet


x
x

Cluster 3, Internet purchasing
Cluster 4, Internet processing


Cluster 5, high Internet

0%
20%
40%
60%
80%
100%
Purchases made Purchases Sales made via Sales processed
via the Internet processed over the Internet over the Internet
the Internet

Figure C-5: Illustration of Internet adoption of purchasing and sales processes

As Figure C-5 illustrates, the following five homogeneous clusters can be
extracted:

-
Cluster 1 (no Internet), with an overall very low usage of the Internet in
purchasing and sales activities.

-
Cluster 2 (little Internet), with low Internet usage to make and process
purchases, but a very low usage in sales activities.

-
Cluster 3 (Internet purchasing), with a high usage of the Internet to make
and process purchases, but a very low usage in sales activities.

-
Cluster 4 (Internet processing), uses the Internet to process purchasing
and sales orders, but does not use it to place or receive orders.

-
Cluster 5 (high Internet), with an overall high adoption of the Internet in
all purchasing and sales activities.

The cluster means for all four categories of Internet usage and the five
identified clusters based on these categories are depicted in Table C-26.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-26: Cluster analysis based on Internet adoption for purchasing and sales
processes, means of clusters

Cluster
no
Internet
little
Internet
Internet
purchasing
Internet
processing
high
Internet
Purchases made via
the Internet 2.78% 22.78% 71.70% 4.43% 90.00%
Purchases processed
over the Internet 1.77% 34.61% 75.40% 67.71% 90.00%
Sales made via the
Internet 0.82% 8.17% 2.62% 3.57% 48.33%
Sales processed over
the Internet 0.89% 14.33% 0.85% 67.43% 71.67%
Cases 78 18 10 7 3

% of all cases 67.24% 15.52% 8.62% 6.03% 2.59%

The cluster with most cases is the one with the lowest adoption rate of the
Internet in purchasing and sales processes. Together with cluster 2, plants that
make little use of the Internet and only then mainly for their purchasing processes
account for 82.76%. Again, the question might have been misleading. This
outcome could be influenced by the perception of the Internet as the WWW. Even
if orders are processed by EDI, respondents might not have made the connection
to the Internet as the underlying TCP/IP driven network that characterizes the
Internet.594

Nevertheless, three plants report a high usage of the Internet in their
purchasing and sales processes. Together with plants that use the Internet
considerably in their purchasing activities and plants that manage purchasing and
sales processes over the Internet, they account for the remaining 17.24%.

Practices and characteristics of these “high Internet adopters” can be compared
to the other manufacturers in order to find out if this yields any benefits for them.
In order to evaluate the performance levels of these two groups, the previously
introduced performance measures are chosen from the HPM dataset.595 However,
differences remain very little and high Internet adopters show either no, or at least
no significant performance advantage over low adopters.

The HPM database provides no information that allows for a comprehensive
analysis of the way external supply chain partners are connected. The questions
that aim at providing this information carry too much ambiguity for such an
analysis. Without explaining more precisely what is meant by structured

594 See section B.III.3.a.
595 See section C.II.3.


E-Business Applications and Practices of Manufacturing Companies

information exchange and a detailed outline of the kind of IT support for such an
exchange, no meaningful results can be obtained. However, it has to be
acknowledged that at the time the HPM scales were developed, this area was less
obvious and no dedicated SCM software and ERP II solutions were available. The
rapid development in this area makes it hard for a research project of this scale
and scope to account for such technological state-of-the-art applications.
Nevertheless, it is likely that the adoption of these applications is not very far
advanced and this area of the questionnaire should be redesigned for possible
following rounds of the HPM project.

3.
Evidence for the Impact of Software Support and ERP Integration on
Manufacturing Supply Chains
In order to exchange electronic information with business partners, it has to be
gathered and processed internally first. ERP systems are a major source for
gathering and providing such information. They are designed to integrate business
functions and allow data sharing across functional boundaries within a company.
As such, ERP systems are considered to be a prerequisite for SCM systems that
broaden the scope of ERP systems to not only integrate cross-functionally but also
to cross entire supply chains and therefore for e-business in general.596

Stand alone business applications already accomplish direct benefits such as
local optimization and business process automation. ERP systems aim at
providing an infrastructure that leverages those capabilities, providing critical
support for strategic and operational management. Consequently, possessing such
leveraged information may prove to be a substantial leverage for overall
competitive advantage.597

Lopez et al. identify major benefits of ERP systems. These include (1) unified
real-time interfaces across the entire corporation, (2) streamlined and accelerated
business processes, (3) increased flexibility through quicker reactions, (4) the
capability to integrate dispersed divisions of a company along with supply chain
activities, and (5) increased possibilities to align own practices with practices
identified by ERP vendors.598 Bendoly and Schoenherr link the benefits of ERP
systems to the Theory of Swift, Even Flow, introduced by Schmenner and Swink
because of its capability of reducing processing time and variances associated with
this. In addition, Bendoly and Schoenherr suggest that ERP systems in themselves

596
Cf. Tarn, Yen and Beaumont: Exploring the rationales for ERP and SCM integration, p.

26.
597 See Bendoly and Schoenherr: ERP system and implementation process benefits.
Implications for B2B e-procurement, p. 317.
598
Cf. Lopez, David et al.: Impact of information technology and e-commerce on supply
chain management: Survey evidence from manufacturing companies in Michigan, in:
Journal of E-Business, Vol. 4 (2004), No. 1, June, p. 2.


Supply Chain Management and E-Business in Manufacturing Companies

may indicate an overall competitive advantage.599 This seems like a bold
statement. It is more reasonable to assume that the process of implementing an
ERP system and accompanying process and structure adjustment are more likely
the source of a possible competitive advantage together with an ERP system.
Without adjustments, benefits do not likely materialize.

Many authors identify customer satisfaction as one indicator for competitive
advantage. In the context of SCM, especially this customer orientation is seen as
crucial for success.600 Because of the synergetic relationship between SCM and
ERP systems, customer satisfaction should also be positively affected by ERP
systems as they can provide higher responsiveness to customer requests due to
better information availability.

Little empirical research is available on the performance impact of ERP
systems. McAfee presents a thorough experimental analysis to show direct,
operational benefits of an ERP implementation for a variety of performance
measures, such as on-time delivery or average lead time per order.601 To conduct a
broader empirical analysis as evidence, the HPM project builds a solid foundation
because it provides detailed information about software applications, ERP
integration, and a variety of performance measures. First, operational performance
of manufacturing plants based on different degrees of software support for
selected business functions are compared. In a second step, companies that show
low software support with little to no integration, i.e. ERP laggards are compared
with ERP system champions that are characterized by an overall high, integrated
software support.

From the questionnaire, 19 software support functions have been identified as
especially relevant in light of the importance of SCM. Appendix 2 provides an
overview of these. Based on these applications, the degree of software support and
ERP integration are determined as a composite index score. The lowest 20.5% for
software support and 26.4% for ERP integration respectively are considered to be
low adopters and the highest 17.6% for software support and 27.8% for ERP
integration are considered to be high adopters.602 Though interdependence is
inevitable, the two groups are not mutually inclusive, i.e. low software support
does not necessarily indicate low ERP integration if the few applications are all

599 See Bendoly and Schoenherr: ERP system and implementation process benefits.

Implications for B2B e-procurement, pp. 307-317.
600 For example, see Stevens: Integrating the supply chain, p.3; and Mentzer et al.:

Defining supply chain management, p. 15.
601 Cf. McAfee: The impact of enterprise information technology adoption on operational

performance: An empirical investigation, pp. 33-52.
602 The commonly adopted first and fourth quartile thresholds are chosen to determine the

cluster thresholds. The thresholds are not equal because threshold categories included

multiple cases and therefore only either all or none can be removed.


E-Business Applications and Practices of Manufacturing Companies

integrated into a ERP system. However, there is no case that shows high ERP
integration but a low software support.

These different groups and constellations are now analyzed in terms of
differences in performance. As performance measures, again the previously
developed performance measures from the three categories subjective single-item,
objective, and subjective multi-item measures are considered.

The following null-hypotheses are formulated:603

H1:
Manufacturing plants with a high degree of software support for relevant
business functions do not show better performance than plants with a low
degree of software support.

H2:
Manufacturing plants with a high degree of software support and a high
degree of ERP integration for relevant business functions do not show
better performance than plants with a low degree of software support and
low ERP integration.

The comparison of low and high software support adopters is depicted in Table
C-27. The analysis suggests that a higher degree of software support indeed yields
a better operational performance. However, based on independent t-tests, only the
higher performance in cycle time between the two groups is significant, with
t(68)=-2.014 and p<0.05; this represents a medium sized effect of r=0.24. The
difference in manufacturing costs is significant only at p<0.1. For these two
measures, the null-hypothesis H1 can be rejected. The other differences hint
towards H1, but require follow-up research. For customer satisfaction, the mean
values show no considerable difference at all, which is in line with the previous
argument, i.e. that integration might lead to positive, direct customer effects, but
software alone does not.

603 Bortz: Statistik für Sozialwissenschaftler, pp. 106-107 for a discussion of hypothesis
building.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-27: Comparison of selected performance measures for low and high software
adopters

low software
support
high software
support
mean diff.
low/high
Manufacturing
costs
Mean -0.179 0.232 0.410(*)
StDev 1.015 1.095
Valid cases 33 37
Conformance to
product
specifications
Mean 0.005 0.244 0.239
StDev 1.078 0.887
Valid cases 34 37
On-time delivery
Mean 0.114 -0.041 -0.155
StDev 0.974 1.012
Valid cases 34 37
Flexibility in
product change
and volume
Mean -0.163 0.032 0.195
StDev 1.165 1.049
Valid cases 33 35
Inventory turn
Mean 0.076 0.062 -0.015
StDev 0.729 1.079
Valid cases 34 35
Cycle time
Mean -0.235 0.218 0.453*
StDev 0.809 1.051
Valid cases 34 36
Product
capability
Mean 0.141 0.174 0.033
StDev 0.942 0.925
Valid cases 33 36
Innovativeness
Mean 0.011 0.333 0.323
StDev 1.082 0.919
Valid cases 32 35
On-time delivery,
in %
Mean 93.20% 93.47% 0.27%
StDev 8.37% 7.12%
Valid cases 26 29
Internal scrap
and rework, in %
Mean 5.80% 5.17% -0.63%
StDev 5.92% 5.82%
Valid cases 26 31
Returned
defective
products, in %
Mean 1.75% 1.52% -0.23%
StDev 2.85% 3.89%
Valid cases 29 32
Customer
satisfaction
Mean 0.043 -0.036 -0.079
StDev 0.900 0.985
Valid cases 36 42
Distinctive
competencies
Mean -0.061 0.050 0.111
StDev 0.975 1.187
Valid cases 36 42


E-Business Applications and Practices of Manufacturing Companies

By combining the degree of software support and ERP integration, it is
possible to identify “low IT adopters” and “high IT adopters”. The latter show
higher values in all performance measures but internal scrap and rework, as can be
observed in Table C-28. The fact that the amount of returned defective products is
lower suggests that internal mistakes are not necessarily prevented but at least
detected which in turn leads to a lower return rate. This is the same phenomenon
as observed in the previous SCM practice analysis.604 The difference in
manufacturing costs between IT low adopters and IT high adopters is significant,
with t(38)=-2.523 and p<0.05; this represents a rather strong effect of r=0.38.
Consequently, H2 can be rejected for manufacturing costs.

From comparing Table C-27 and Table C-28, it becomes evident that the
difference in customer satisfaction between low adopters and high adopters
increases by including the ERP integration perspective, though not on a significant
level. Therefore, the ERP effect without considering software application adoption
is tested. Because of the relatively narrow spread within the composite software
adoption index, relatively high software adopters – though not identified as high in
the software adoption cluster due to this stricter separation – that show high ERP
integration are excluded from the analysis in Table C-28. Consequently, the sole
effect of ERP integration on customer satisfaction is tested separately, as stated in
H3.

H3:
Manufacturing plants with a high degree of ERP integration for relevant
business functions do not show higher customer satisfaction than plants
with a low degree of ERP integration.

604 See section C.II.3.


Supply Chain Management and E-Business in Manufacturing Companies

Table C-28: Comparison of selected performance measures for overall low and high IT
adopters

low software, low
ERP integration
high software, high
ERP integration
mean diff.
low/high
Manufacturing
costs
Mean -0.350 0.441 0.791*
StDev 1.039 0.944
Valid cases 19 21
Conformance to
product
specifications
Mean -0.068 0.207 0.275
StDev 1.056 0.870
Valid cases 20 21
On-time delivery
Mean 0.207 0.037 -0.170
StDev 0.964 0.970
Valid cases 20 21
Flexibility in
product change
and volume
Mean -0.115 0.083 0.198
StDev 1.282 0.761
Valid cases 19 19
Inventory turn
Mean 0.164 0.254 0.090
StDev 0.755 0.985
Valid cases 20 19
Cycle time
Mean -0.254 0.269 0.523(*)
StDev 0.938 1.064
Valid cases 20 20
Product capability
Mean 0.102 0.037 -0.065
StDev 0.939 0.782
Valid cases 20 20
Innovativeness
Mean -0.064 0.407 0.471
StDev 1.127 0.817
Valid cases 19 21
On-time delivery,
in %
Mean 93.69% 95.44% 1.75%
StDev 8.55% 4.06%
Valid cases 17 16
Internal scrap and
rework, in %
Mean 5.47% 5.50% 0.03%
StDev 6.69% 6.49%
Valid cases 17 18
Returned
defective
products, in %
Mean 2.28% 1.68% -0.60%
StDev 3.39% 4.92%
Valid cases 17 16
Customer
satisfaction
Mean -0.050 0.231 0.281
StDev 0.910 0.886
Valid cases 22 23
Distinctive
competencies
Mean -0.200 -0.072 0.127
StDev 0.996 1.322
Valid cases 22 23


Evidence for Superior Performance of SCM Integration Champions

Based on an independent t-test, the difference in customer satisfaction
performance between plants with low ERP integration and those that show a high
degree of ERP integration is significant, with t(94)=-3.005 and p<0.01, as shown
in Table C-29. Therefore, H3 can be rejected. It can be concluded that a high
degree of ERP integration indeed leads to higher customer satisfaction. The fact
that a high degree of software support alone does not show this effect indicates
that the positive effects of ERP integration reach beyond company boundaries.

Table C-29: Customer satisfaction for plants with low and high ERP integration

low ERP
integration
high ERP
integration
mean diff.
low/high
Customer
satisfaction
Mean -0.345 0.225 0.570**
StDev 0.905 0.950
Valid cases 47 49

This analysis shows that ERP integration contributes significantly to customer
satisfaction. Since customer satisfaction aggregates overall performance up to this
point in a supply chain, ERP integration can be seen as a beneficial tool for supply
chain success and therefore proves to be an important factor for competitive
advantage for manufacturing companies. Furthermore, the analysis also suggests
that software support improves operational performance in selected areas, such as
manufacturing costs and cycle time independently from ERP integration.
However, ERP high adopters who combine a high degree of software support and
integrate those applications in a comprehensive ERP system show an even better
performance. As a consequence, those 26 high ERP adopters that do not fall into
the high software support category can excel by extending their overall software
support.

Although the results show more support towards the hypotheses, many of the
performance differences are not significant. This is partly due to a rather small
sample size after clustering the groups. Furthermore, due to the descriptive nature
of our analysis, no causal links can be established. Thus, other factors such as
SCM practices that might be of importance are not considered. This omission will
be picked up in section D, which investigates causal relationships between SCM
practices, ERP integration, and performance.

IV. Evidence for Superior Performance of SCM Integration Champions
So far, SCM practices have been considered independently from e-business
capabilities. For SCM practices, four constructs that define SCM practices within
manufacturing plants have been operationalized. The management of the
integration of seamless material and information flows could not be captured
based on data available from the HPM project. Therefore, ERP integration is now


Supply Chain Management and E-Business in Manufacturing Companies

considered as a substitute to reflect integration at least within the plants. As
pointed out in the previous section, ERP systems serve as the backbone for further
SCM system adoption and therefore provide a seamless information flow across
company borders.

In order to derive empirical evidence for the relationship between practices and
performance, respondents of the HPM study are divided into four groups along the
dimensions degree of ERP integration and SCM practices adoption as defined
through the previous cluster analysis.605 The groups are well separated as can be
observed in Figure C-6. Omitting all other plants that are positioned between
these four groups provides a clearer distinction of the four groups identified
below:

-
SCM laggards. In this category, plants that fall into the first quartile in
terms of ERP integration and plants that belong to the low SCM practice
cluster or show overall low SCM practice with an internal orientation are
considered. In total, 24 plants are identified that show these
characteristics.

-
SCM champions. Plants that belong to the fourth quartile in terms of ERP
integration and that show the highest SCM practice among all plants as
identified by the cluster analysis in section C.II.3 are identified as SCM
champions. Because of this strict selection, only true SCM champions are
identified and only 11 plants qualify for this group.

-
ERP savvy plants. Plants that show overall a low SCM practice adoption
or low SCM practice with an internal orientation but belong to the top
quartile with regard to ERP integration belong to this category. They are
therefore considered to be technology-driven in the context of SCM.
There are 23 respondents identified as ERP savvy plants.

-
SCM advocates. In this group, plants show commitment to SCM practices
without a high degree of technology support compared to other
manufacturers. Plants that show medium SCM practices with a high
commitment to external collaboration or high SCM practice adopters and
plants that belong to the first two quartiles with regard to ERP integration
are investigated. In total, 49 plants are identified as SCM advocates.

Performance of manufacturing plants can now be investigated to find out
whether differences between these four groups are evident and to what extent the
explicit consideration of ERP integration provides additional insights.

605 See section C.II.3.


Evidence for Superior Performance of SCM Integration Champions

4th
quartile
2 SCM
integrationchampions
3
ERP savvy
plants
23 plants 11 plants
3rd

quartile

Degree
ofERP
Integration
1st
quartile
2nd
quartile
1
SCM
laggards
4
SCM
advocates
24 plants 49 plants
low SCM internal SCM external SCM high SCM
practice orientation orientation practice

SCM Practices Adoption

Figure C-6: Portfolio classification according to SCM integration capabilities

The hypothesis based on these four proposed groups is that SCM champions
generally show a better performance than SCM laggards. Therefore, H4 states as
the following:

H4:
Those plants that show a high degree of SCM practice and a high degree
of ERP integration (SCM champions) do not perform better than plants
with a low degree of SCM practice and a low degree of ERP integration
(SCM laggards).

Starting from the top of Table C-30, all subjective single-item performance
measures show better values for SCM champions. However, the differences
between the two groups are not statistically significant.606 Two measures –
flexibility in product change and volume and cycle time – are significant on a
lower level, i.e. p<0.1. Nevertheless, the magnitude of the mean differences

606 Statistical significance is accomplished with p<0.05.


Supply Chain Management and E-Business in Manufacturing Companies

provides an indication for a better performance of SCM champions. The greatest
difference is observed in flexibility. High levels of SCM and internal integration
of software support seem to enable plants to react more quickly to market
demands and variations in terms of product changes and volume adjustments.
Furthermore, cycle time seems to benefit especially from this combination as this
shows the second highest mean difference among this performance measure
category. It is likely that cycle time benefits especially from better planning
capabilities of SCM champions compared to SCM laggards. This enables plants to
better schedule their production flows, the associated procurement process, and
resource allocations. Better performance in inventory turnover and lower
manufacturing costs can draw on the same characteristics.

Besides higher efficiency, innovativeness and product capability also seem to
benefit. One could assume that especially cooperative skills lead to this sort of
performance improvements instead of IT capabilities and that efficiency gains can
be attributed especially to the combination of SCM practices and ERP integration.
A closer look at mean differences between all groups reveals, however, that the
differences between ERP savvy plants and SCM advocates are not very high with
regard to innovativeness and product capability. In fact, ERP savvy plants even
show a slightly better performance. In contrast, SCM advocates seem to benefit
relatively more in efficiency performance measures compared to ERP savvy
plants.


Evidence for Superior Performance of SCM Integration Champions

Table C-30: Performance overview SCM integration groups

SCM
laggards (1)
SCM
champions (2)
mean
diff. 1/2
Manufacturing
costs
Mean -0.024 0.413 0.437
StDev 0.926 1.013
Valid cases 21 11
Conformance to
product
specifications
Mean -0.043 0.207 0.250
StDev 1.008 0.870
Valid cases 22 11
On-time delivery
Mean -0.170 -0.064 0.106
StDev 1.308 0.876
Valid cases 22 11
Flexibility in
product change
and volume
Mean -0.257 0.441 0.698(*)
StDev 1.139 0.860
Valid cases 22 11
Inventory turn
Mean -0.091 0.459 0.550
StDev 0.984 0.960
Valid cases 22 11
Cycle time
Mean -0.225 0.370 0.595(*)
StDev 0.919 1.029
Valid cases 22 11
Product capability
Mean -0.192 0.220 0.412
StDev 1.051 0.698
Valid cases 22 11
Innovativeness
Mean -0.243 0.259 0.502
StDev 1.158 0.666
Valid cases 22 11
On-time delivery,
in %
Mean 92.66% 96.17% 3.51%
StDev 8.77% 3.14%
Valid cases 16 10
Internal scrap and
rework, in %
Mean 2.21% 4.86% 2.65%(*)
StDev 3.63% 3.58%
Valid cases 17 9
Returned
defective
products, in %
Mean 2.81% 1.37% -1.44%
StDev 3.95% 3.07%
Valid cases 16 10
Customer
satisfaction
Mean -0.793 1.247 2.040***
StDev 0.876 0.533
Valid cases 24 11
Distinctive
competencies
Mean -0.525 0.690 1.215***
StDev 1.034 0.930
Valid cases 24 11

ERP savvy SCM
plants advocates
-0.127 0.074
1.110 1.054
21 43
0.142 0.143
0.920 1.121
21 43
-0.018 0.393
0.849 0.903
21 43
-0.303 0.098
0.879 1.133
20 42
-0.111 0.345
1.077 0.919
20 42
-0.021 0.135
1.061 1.127
21 42
-0.145 0.073
0.879 1.098
21 44
-0.014 -0.093
0.961 1.031
21 40
93.78% 93.42%
6.26% 7.33%
17 37
6.26% 5.84%
7.25% 6.42%
20 35
2.52% 1.06%
5.23% 1.45%
14 36
-0.346 0.363
0.783 0.818
23 49
-0.494 0.107
0.995 1.095
23 49


Supply Chain Management and E-Business in Manufacturing Companies

Reviewing objective performance measures reveals that SCM champions show
a higher percentage of on-time deliveries compared to all the other three groups.
Apparently, only the combination of SCM practices and ERP integration actually
improves on-time delivery, a quality which has been also considered as a
subjectively assessed performance measure. Comparing the subjective assessment
with the objective data reveals some inconsistencies. Whereas SCM advocates
believe that they perform better than their competitors, this cannot be confirmed
objectively. This might be due to difficulties in evaluating on-time delivery
performance of competitors. Although this can also be said for all other seven
performance measures of this category, on some measures it is much easier to
obtain competitor information. For example, on product capability,
innovativeness, flexibility, or cycle time, feedback and information through
industry insiders are likely to be more precise than on on-time delievery
performance. Consequently, plant managers tend to be better in assessing these
than on-time delivery performance.

Internal scrap and rework is more than 50% lower for SCM laggards than for
SCM champions. This may seem surprising; however, because SCM laggards are
also characterized by a low level of internal coordination and collaboration, they
might not only detect fewer mistakes throughout the process but also not record
costs associated with detected in-process mistakes. SCM champions perform
better in revealing internal scrap and rework and because of their higher
commitment towards collaboration, they also record these properly.607 SCM
advocates and ERP savvy plants show an even higher level of internal scrap and
rework. This indicates that both groups are also better in detecting and recording
these measures but are less effective in using this information for reducing scrap
and rework. For ERP savvy plants, it might be that information is gathered and
recorded efficiently but not processed managerial to reduce scrap and rework.
SCM advocates might be not equipped with the necessary IT support and
integration to improve performance beyond levels that can be achieved through
coordination and especially through external collaboration alone.

Effectiveness with regard to delivering quality products is best assessed by the
percentage of returned defective products. These products have been considered
internally as being flawless and in accordance to quality standards and customer
expectations. The fact that products are returned as defective, however, not only
shows that it has not been the case in the first place but also impacts customer
perception negatively. SCM champions with 1.37% returned defective products
show a 51.2% lower return rate than SCM laggards. Also, ERP capabilities alone
do not positively influence this performance measure much. SCM advocates,
however, report on average an even lower return rate of 1.06% than SCM
champions. This leads to the assumption that a high degree of SCM practice

607 This finding is in line with the previous analysis.


Evidence for Superior Performance of SCM Integration Champions

adoption affects delivered quality positively, an assumption which has been also
found in the previous analysis of SCM practices by themselves.608

The last group of performance measures contains the subjective, multi-item,
multi-informant measures customer satisfaction and distinctive competencies.
SCM champions show a significantly higher level of customer satisfaction than
SCM laggards, with a mean difference of 2.040, t(33)=-7.072, and p<0.001. From
all previously conducted analyses, identifying SCM champions and laggards
segregates integrative SCM practices most appropriately and explains different
levels of customer satisfaction best. High levels of customer satisfaction as a
reflection of an overall superior, customer oriented performance can only be
achieved by plants that apply a high level of all SCM components, namely internal
coordination and collaboration, external coordination and collaboration, and
internal integration of e-business modules. Though it is not possible to test
external integration based on the HPM database, it can be suspected that this last
missing link only further strengthens the other elements. What also becomes
evident is that SCM advocates are better suited to achieve higher levels of
customer satisfaction than ERP savvy plants. This suggests that the best path of
improvement for SCM laggards is to build managerial SCM capabilities first and
then focus on integrating these.

Distinctive competencies support the findings regarding customer satisfaction.
Not only does superior customer satisfaction performance benefit from integrative
SCM but also does a combination of selected distinctive competencies. Therefore,
integrative SCM shows the same effect on better competencies in the areas of
supplier and customer relations, ERP, quality improvement, SCM, and JIT as on
customer satisfaction, though to a lower degree.

Overall, it can be concluded that there is evidence that integrative SCM
practices – reflected by SCM champions – indeed lead to higher performance. Its
holistic, systemic impact is best reflected by the significantly higher performance
levels of SCM champions regarding the scales customer satisfaction and
distinctive competencies. Differences in the characteristics of ERP savvy plants
and SCM advocates regarding returned defective products suggests a path of
improvement that first leads to strengthening and building SCM practices and then
to pursuing integration supported by IT capabilities in form of ERP integration.
This way, companies are likely to achieve relatively quick performance
improvements. The often referred to “worse-before-better” effect can be
mitigated. This builds the foundation for sustaining motivation for further
improvement efforts.

608 See section C.II.3.c.


D. Analysis
of the Supply Chain Management
Framework
Up to this point, general important implications of SCM practices and e-business
adoption have been derived through cluster analysis and mean comparisons. In
order to analyze the SCM framework developed in section B.I.3.c. and its
implications, more complex analyses are necessary. Structural equation modeling
(SEM) is capable of doing this. In general, SEM allows estimating a hypotheses
model in its entirety and therefore considers hypothesized relationships in its
overall estimation.609 In this analysis, the partial least squares (PLS) method has
been chosen to conduct SEM for the SCM framework. Since this methodology is
not as common as other causal modeling techniques, such as LISREL, first PLS is
introduced and subsequently, the SCM framework is estimated using the PLS
methodology and implications are drawn upon the results.

I.
Path Model Estimation Using Partial Least Squares (PLS)
Structural equation models (SEM) have been gaining popularity since the
beginning of the 1970s.610 They are characterized by evaluating relationships
between latent, i.e. not directly measurable, variables.611 These are conceptualized
by indicators that reflect or influence them. The partial least square (PLS) method
is a variance-based causal modeling approach, developed in the 1960s by Herman
Wold.612 In contrast to PLS, most other SEM are covariance-based, with LISREL
(Linear Structural Relation) being the most prominent.613 Both approaches are

609
See Homburg, Christian and Lutz Hildebrandt: Die Kausalanalyse: Bestandsaufnahme,
Entwicklungsrichtungen, Problemfelder, in: Hildebrandt, Lutz and Christian Homburg
(Eds.): Die Kausalanalyse: Instrumente der empirischen betriebswirtschaftlichen
Forschung, Stuttgart 1998, pp. 17-19.

610
See Homburg and Baumgartner: Beurteilung von Kausalmodellen - Bestandsaufnahme
und Anwendungsempfehlungen, pp. 162-176.

611
Cf. Herrmann, Andreas, Frank Huber and Frank Kressmann: Partial least squares - Ein
Leitfaden zur Spezifikation, Schätzung und Beurteilung varianzbasierter
Strukturgleichungsmodelle, forthcoming in: Zeitschrift für betriebswirtschafltiche
Forschung, Vol. 58 (2006), No. 2, p. 35.

612
One of the first publications of the PLS method can be found in Wold, Herman: Path
models with latent variables: The NIPALS approach, in: Blalock, Hubert M. (Ed.):
Quantitative sociology: International perspectives on mathematical and statistical
modeling, New York 1975.

613
See Fornell, Claes and Fred L. Bookstein: Two structural equation models: LISREL
and PLS applied to consumer exit-voice theory, in: Journal of Marketing Research, Vol.
19 (1982), No. 4, November, pp. 440-452.


Analysis of the Supply Chain Management Framework

used to analyze similar models614 and PLS and LISREL are considered to be
complementary rather than competitive.615 After the introduction of PLS, it will be
compared to LISREL in order to point out important differences and the
conditions under which the application of each is more suitable. Finally, the
methodological application and interpretation of PLS, including procedures how
to validate models, will be illustrated.

1. Partial Least Squares as a Causal Modeling Technique
PLS is a so-called “second generation” modeling technique616 that covers and
extends traditional, “first generation”, analysis techniques such as canonical
correlation, redundancy analysis, multiple regression, multivariate analysis of
variance, and factor analysis in order to formulate and estimate more complex
path models.617 One of the main problems under which those traditional, first
generation multivariate analysis methods618 suffer compared to second generation
techniques is that “the measurement model, analogous to factor analysis, is tested
independently of the structural model, created by regression. Thus, a maximally
efficient fit between the data and a structural model is not likely to occur.”619 This
deficit is addressed by SEMs such as LISREL and PLS as they combine the
elements of path and factor analysis in one comprehensive model.620

The variance-based PLS is the preferred method if researchers are interested in
a good explanation of changes or in the prediction of objective variables, as it is
able to give explanations of variances. Furthermore, it is more suitable than other

614 Cf. Fornell and Bookstein: Two structural equation models: LISREL and PLS applied
to consumer exit-voice theory, pp. 449 et sqq.

615
See Wold, Herman: Soft modeling: The basic design and some extensions, in: Jöreskog,
Karl G. and Herman Wold (Eds.): Systems under indirect observation, Part II,
Amsterdam 1982, pp. 1-54.

616
For example, cf. Chin, Wynne W.: The partial least squares approach to structural
equation modeling, in: Marcoulides, G. A. (Ed.): Modern methods for business
research, Mahwah 1998, p. 296.

617
Cf. Chin, Wynne W., Barbara L. Marcolin and Peter R. Newsted: A partial least squares
latent variable modeling approach for measuring interaction effects: Results from a
Monte Carlo simulation study and an electronic-mail emotion / adoption study, in:
Information Systems Research, Vol. 14 (2003), No. 2, June, Appendix A, p. 5.

618
See Backhaus et al.: Multivariate Analysemethoden for a comprehensive coverage of
multivariate analysis methods.

619
Amoroso, Donald L. and Paul H. Cheney: Testing a causal model of end-user
application effectiveness, in: Journal of Management Information Systems, Vol. 8
(1991), No. 1, Summer, p. 77.

620
See Hildebrandt, Lutz: Kausalanalytische Validierung in der Marketingforschung, in:
Hildebrandt, Lutz and Christian Homburg (Eds.): Die Kausalanalyse, Stuttgart 1998, p.
87 and p. 95.


Path Model Estimation Using Partial Least Squares (PLS)

techniques in the theory-generation process because the inclusion of indicators
with uncertain validity is less problematic with regard to the overall model
estimation.621

PLS consists of indicator variables and latent variables, with latent variables
being constructs of these indicators. The relationships between the indicator
variables and the latent variables are specified by the measurement (outer) model,
whereas the relationships between latent variables are specified by the structural
(inner) model. Both models are estimated together.622 Figure D-1 provides a
schematic overview of a PLS measurement model.


Inner model

x1
x2
x3
x4
y1
y2
y3
y4
..px1
px2
px3
px4
.y4
.y3
.y2
.y1...1
e1
e2
e3
e4
r14
r12
r23
r34
r13
r24
Outer model Outer model
Figure D-1: Schematic PLS model.623

There exist two different kinds of indicators: reflective indicators and
formative indicators.624

621
Cf. Herrmann, Huber and Kressmann: Partial least squares - Ein Leitfaden zur
Spezifikation, Schätzung und Beurteilung varianzbasierter Strukturgleichungsmodelle,

p.45. For more details on model selection, see section D.I.2.
622 Barclay et al. provide a thorough overview of PLS, see Barclay, D.W., C. Higgins and
R. Thompson: The partial least squares (PLS) approach to causal modeling: Personal
computer adaptation and use as an illustration, in: Technology Studies, Vol. 2 (1995),
No. 2, pp. 285-309; see also Hulland, John: Use of partial least squares (PLS) in
strategic management research: A review of four recent studies, in: Strategic
Management Journal, Vol. 20 (1999), No. 2, p. 196.
623
The schematic figure is adopted from Herrmann, Huber and Kressmann: Partial least
squares - Ein Leitfaden zur Spezifikation, Schätzung und Beurteilung varianzbasierter
Strukturgleichungsmodelle, pp. 36-37; as usual for structural equation models, Greek
symbols are used according to the LISREL model, cf. Backhaus et al.: Multivariate
Analysemethoden, pp. 348 et sqqq.


Analysis of the Supply Chain Management Framework

Reflective indicators, or effect indicators, are reflections of the extent a latent
variable is characterized, but they do not directly influence them.625 Therefore,
they could be exchanged without a loss of validity if a better way is indicated to
reflect a latent variable. As an example, in order to find out the health status of a
person, a set of questions can indicate this. Although, there is an almost infinitely
large number of questions that can determine a health status, only few are
necessary to derive a reliable answer. To illustrate reflective indicators in a PLS
model, arrows point from the latent variable to the indicators, as shown in Figure
D-1 for indicators x1 – x4 and the latent variable .. Reflective indicators can be
tested by means of factor analysis. In the PLS model, weights px1 – px4 are
assigned to the loadings. These weights are the results of the overall model
estimation.

In contrast to reflective indicators, formative indicators determine a latent
variable directly. Formative indicators, also called cause indicators, are often
neglected.626 Changing a formative indicator also changes the actual construct
value. Therefore, they cannot be excluded without a strong theoretical
justification. As an example, in order to determine a health factor for a specific
task, a certain number of health indicators return a health factor. These indicators
are carefully chosen as they specifically determine health suitability for this task.
Such a case could be determining the specific health suitability for astronauts.
Consequently, removing an indicator alters the construct substantially. Such an
indicator, previously identified as important, would be missing and the overall
result would not be valid anymore. Generally, a formative indicator cannot be
removed, only exchanged if another indicator measures the same underlying fact.

With formative indicators, index variables or composite measures can be built.
One example where formative indicators were chosen intentionally is provided by
Homburg, Workman, and Krohmer, who formed a market complexity measure for
their analysis.627 More examples are provided by Diamantopoulos and
Winklhofer.628 Although formative indicators can be correlated positively,

624
Unless stated otherwise, the following explanations of reflective and formative
indicators and selection criteria are mainly based on: Herrmann, Huber and Kressmann:
Partial least squares - Ein Leitfaden zur Spezifikation, Schätzung und Beurteilung
varianzbasierter Strukturgleichungsmodelle, pp. 46-49.

625
Cf. Bollen, Kenneth A.: Structural equations with latent variables, New York et al.
1989, p. 65.

626
Bollen: Structural equations with latent variables, p. 65.

627
See Homburg, Christian, John P. Workman and Harley Krohmer: Marketing's influence
within the firm, in: Journal of Marketing, Vol. 63 (1999), No. 4, April, pp. 1-17.

628
Cf. Diamantopoulos, Adamantios and Heidi M. Winklhofer: Index construction with
formative indicators: An alternative to scale development, in: Journal of Marketing
Research, Vol. 38 (2001), No. 2, p. 270. This publication provides also a thorough
analysis on selection and analysis of formative indicators.


Path Model Estimation Using Partial Least Squares (PLS)

negatively, or be neutral to each other, generally they should not be correlated on a
significant level. In cases where indicators are significantly correlated, it is
possible that the indicators contain redundant information.629 Such redundancy is
not a problem for reflective measures. They are always required to be positively
correlated. The correlation between formative indicators is displayed in the
schematic PLS model in Figure D-1 through the r symbol and the respective
indicator numbers as subscript. To illustrate formative indicators in a PLS model,
arrows point from the indicators to the latent variable, as can be seen in Figure D1
for the indicators y1 – y4 and the latent variable .; .y1 – .y4 represent the weights
assigned to the indicators after model estimation. The variable r shows the
correlation between each indicator of a formative measure.

One of the strengths of the PLS approach is that it is capable of not only
including reflective indicators, but also formative indicators. With respect to
reflective indicators, the selection of appropriate indicators that reflect the
corresponding latent variables is subject to debate among researchers. Whereas
some researchers suggest using quantitative methods in order to identify relevant
indicators, while others argue that only theoretical and logical considerations and
justifications should be the basis for the proper indicator selection.630 Furthermore,
the decision between the formative and the reflective nature of the indicators
should follow the causal reasoning between indicators and construct. Though
statistical methods may assist in identifying relevant indicators, the substantive
knowledge and theoretical justification should be the major concern.631

At this point, a reminder about the nature of causal modeling seems to be
appropriate. Despite the fact that the term “causal” implies that causal models are
able to identify causal relationships, this is not entirely true. Such causal
relationships are believed to be only testable in experimental settings.632
According to Sterman, other methods to determine reliable causality are
randomized, double-blind trials; large samples; long-term follow-up studies;
replication; and statistical interference. Sterman also remarked that the application
of such reliable methods in social and human sciences is difficult, rare, and in fact
often impossible.633 Statistical correlation as means to deduce causality, however,
has to be preceded by a combination of strong theoretical foundation, logical

629 Cf. Diamantopoulos and Winklhofer: Index construction with formative indicators: An

alternative to scale development, p. 272.
630 See Rossiter, John: The C-OAR-SE procedure for scale development in marketing, in:

International Journal of Research in Marketing, Vol. 19 (2002), pp. 318 et sqq.
631 Cf. Chin, Wynne W. and Peter A. Todd: On the use, usefulness, and ease of use of

structural equation modeling in MIS research: A note of caution, in: MIS Quarterly,

Vol. 19 (1995), June, pp. 239-240.
632 Cf. Homburg and Hildebrandt: Die Kausalanalyse: Bestandsaufnahme, Entwick


lungsrichtungen, Problemfelder, p. 17.
633 Cf. Sterman, John D.: Business Dynamics, Boston 2000, p. 142.


Analysis of the Supply Chain Management Framework

considerations and common sense. Moreover, it can only reject or support
causality, but never by itself ultimately confirm it.634

These pre-considerations are necessary in order to formulate appropriate
hypotheses for causal relationships. Also, as mentioned before, experimental tests
can be included in these pre-considerations. Then, correlation analysis or SEMs
can test the formulated hypotheses in order to support, not confirm, or to reject
them.635 This directly leads to the scientific requirement to formulate hypotheses
as null-hypotheses, i.e. as negative or inverse hypotheses. Since hypotheses can
only be rejected, positively formulated hypotheses could never be confirmed.
However, formulated as null-hypotheses, they can be rejected. In a strict scientific
manner, therefore, using null-hypotheses is the most accurate method. Ideally,
these formulations are precise enough so that rather clear implications can be
derived.636 Obviously, the more restrictive hypotheses are formulated the harder it
will be to reject them.637

This said, it has to be noted that it is still common practice in the scientific
community to use positively formulated hypotheses that are then, in a strict
scientific manner, falsely confirmed. This could be attributed to linguistic style.
As long as one is aware of the restrictions described above, this does not have be
of too much concern.638

The general concern of using causal terminology is a rather old one. Blalock
has remarked in 1968 that sociologists tried to avoid causal terminology by using

634
There might exist other, not observed dominant factors influencing the correlated
measures that in fact define the observed correlation, cf. Bortz: Statistik für
Sozialwissenschaftler, p. 217 and p. 239.

635
Cf. Bortz: Statistik für Sozialwissenschaftler, p. 217.

636
See Chmielewicz, Klaus: Forschungskonzeptionen der Wirtschaftswissenschaft, 3rd
ed., Stuttgart 1994, pp. 100-105 for a more detailed discussion on the necessity to
differentiate between verification and falsification.

637
Vaguely formulated hypotheses allow for more distinct research constellations.
Therefore, it is then generally easier to find exceptional constellations that allow to
reject a hypothesis.

638 For several examples of this practice, see the following publications: Bharadwaj,
Anandhi S.: A resource-based perspective on information technology capability and
firm performance: An empirical investigation, in: MIS Quarterly, Vol. 24 (2000), No. 1,

p. 176; Sanders and Premus: Modeling the relationship between firm IT capability,
collaboration, and performance, pp. 4-5; Wisner, Joel D.: A structural equation model
for supply chain management strategies and firm performance, in: Journal of Business
Logistics, Vol. 24 (2003), No. 1, pp. 6-7; Sanders and Premus: Modeling the
relationship between firm IT capability, collaboration, and performance, pp. 4-5; Duffy,
Richard and Andrew Fearne: The impact of supply chain partnerships on supplier
performance, in: The International Journal of Logistics Management, Vol. 15 (2004),
No. 1, p. 61.

Path Model Estimation Using Partial Least Squares (PLS)

the terms structures and functions instead. However, this did not fundamentally
solve the problem as it merely gave it another name.639 Nevertheless, this
controversy seems to be ongoing. In order to avoid unnecessary complications, it
seems appropriate to follow Homburg and Hildebrandt’s suggestion to continue
using causal terminology, keeping these necessary remarks in mind.640

Returning to the PLS methodology, PLS assesses the relationships between
constructs and their indicators, and among constructs with the aim to minimize
error variance. In doing so, it “assesses the predictive relationships in the model
and tests how well one part of the model predicts values in other parts.”641 The
estimation of the PLS model is conducted in three stages:642

1.
The first stage consists of an iterative estimation of weights and latent variable
scores. Based on a random start matrix of outside approximation, first inner
weights are estimated, followed by an inside approximation. Then, the outer
weights are determined, followed by an outside approximation. This
procedure continues until no further changes occur and therefore convergence
is obtained.
2.
In stage two, factor loadings and path coefficients are estimated using ordinary
least square regression.
3.
In stage three, the location parameters of the linear regression functions are
estimated.
Wynne W. Chin and Peter R. Newsted summarize this procedure as follows:

“The PLS procedure is then used to estimate the latent variables as an exact
linear combination of its indicators with the goal of maximizing the
explained variance for the indicators and latent variables. Following a series
of ordinary least squares analyses, PLS optimally weights the indicators such
that a resulting latent variable estimate can be obtained. The weights provide
an exact linear combination of the indicators for forming the latent variable
score which is not only maximally correlated with its own set of indicators

639
Cf. Blalock, Hubert M.: Theory building and causal interferences, in: Blalock, Hubert

M. and Ann B. Blalock (Eds.): Methodology in social research, New York St. Louis
London 1968, p. 162.640 Cf. Homburg and Hildebrandt: Die Kausalanalyse: Bestandsaufnahme, Entwicklungsrichtungen,
Problemfelder, p. 17.
641
Ranganathan, C., Jasbir S. Dhaliwal and Thompson S.H. Teo: Assimilation and
diffusion of web technologies in supply-chain management: An examination of key
drivers and performance impacts, in: International Journal of Electronic Commerce,
Vol. 9 (2004), No. 1, p. 145.

642
Based on Lohmöller, Jan-Bernd: Latent path modeling with partial least squares,
Heidelberg 1989, pp. 30-31. The reader is referred to this source for a detailed
description of the procedure.


Analysis of the Supply Chain Management Framework

(as in component analysis), but also correlated with other latent variables
according to the structural (i.e. theoretical) model.”643

An unfavorable characteristic of PLS is the fact that constructs, i.e. latent
variables, incorporate the measurement error of its indicators. Therefore, construct
values and model parameter estimates based on those are inconsistent.644 Since
construct values are closer to their indicators, these relationships are overestimated
whereas relationships between constructs are underestimated. Nevertheless, these
estimates are considered to be conservative. However, the prediction quality of
PLS remains untouched as these two effects approximately level out. Furthermore,
the order of effects and their relation to each other remain almost proportional.645
In order to return fairly correct estimates, the number of indicators per latent
variable should be large enough or the indicator loadings should display values
greater than 0.8.646 This is referred to as “consistency at large”. Under these
conditions and compared to other methods, large sample sizes are not required.647

2.
Comparison of Partial Least Squares with Linear Structural Relation
(LISREL) Modeling
The application of LISREL models is more common than the application of PLS
ones. Ringle attributes this to the fact that no adequate software was available until
PLS-Graph 3.0, developed by Wynne W. Chin, with an easy to use interface
became available.648 Another software application has also become available,

643
Chin, Wynne W. and Peter R. Newsted: Strutural equation modeling analysis with
small samples using partial least squares, in: Hoyle, Rick H. (Ed.): Strategies for small
sample research, Thousand Oaks, CA 1999, p. 26.

644
For a more detailed treatment of this topic, see Herrmann, Huber and Kressmann:
Partial least squares - Ein Leitfaden zur Spezifikation, Schätzung und Beurteilung
varianzbasierter Strukturgleichungsmodelle, pp. 40-41; Fornell, Claes and Jaesung Cha:
Partial least squares, in: Bagozzi, Richard P. (Ed.): Advanced methods of marketing
research, Cambridge, MA 1994, 66.

645
Cf. Herrmann, Huber and Kressmann: Partial least squares - Ein Leitfaden zur
Spezifikation, Schätzung und Beurteilung varianzbasierter Strukturgleichungsmodelle,

p. 41.
646
Cf. Chin, Marcolin and Newsted: A partial least squares latent variable modeling
approach for measuring interaction effects: Results from a Monte Carlo simulation
study and an electronic-mail emotion / adoption study, Appendix D, p. 10.

647
Chin, Marcolin and Newsted: A partial least squares latent variable modeling approach
for measuring interaction effects: Results from a Monte Carlo simulation study and an
electronic-mail emotion / adoption study, Appendices A, D, pp. 6-10.

648
Cf. Ringle, Christian M.: Messung von Kausalmodellen, Industrielles Management
Working Papers (No. 14) 2004, Hamburg, p. 28. Though there was a DOS-based
program available, LVPSL, programmed by Jan-Bernd Lohmöller, it was relatively
complicated to use. Unfortunately, no official service of PLS-Graph 3.0 is available, but


Path Model Estimation Using Partial Least Squares (PLS)

SmartPLS 2.0, which is fairly similar to PLS-Graph. Since it is a Java-based
program, it is even easier to use and provides additional features, for example it
can process larger models. Furthermore, it is freely available. SmartPLS is
currently under development and new releases are frequently provided.649

As already pointed out, LISREL is a covariance-based method whereas PLS is
a variance-based approach. LISREL is of confirmatory nature as it tests a model
and produces goodness-of-fit measures that explain how well the observed data
corresponds to the theoretical model; i.e. LISREL attempts to explain observed
covariance. However, the requirements and assumptions towards the data are
rather restrictive. For example, data have to be normally distributed and rather
large sample sizes are required. In contrast, PLS does not perform as well on
parameter estimation but in turn is able to explain variances, the extent to which
latent variables relate to each other, and the extent to which indicators are able to
describe a construct. Thus, PLS analysis puts emphasis on the estimation of the
relation weights.650 Additionally, PLS requires smaller sample sizes and has no
demands in terms of data distribution. Table D-1 compares PLS and covariance-
based methods.651 Researchers should be aware of the attributes of both
covariance-based and variance-based methods before choosing one over the
other.652

Fornell and Bookstein remarked that “[…] the choice between LISREL and
PLS is neither arbitrary nor straightforward.”653 In fact, they were among the first
that summarized the different characteristics of LISREL and PLS. In the study
under consideration here and described in section D.III., PLS is the appropriate
measurement method. Firstly, the degree of influence of the constructs on each
other and on performance is of interest. Only then can the model provide decision
making support for manufacturing companies as to where to set priorities in
improvement initiatives. Secondly, the underlying theory has not been fully
developed yet. Furthermore, though the survey design is theoretical and concept

at the time of this text, the following webpage provided information for interested Beta-
users: http://disc-nt.cba.uh.edu/plsgraph/, retrieved on January 27, 2006.

649
Cf. Hansmann, Karl-Werner and Christian M. Ringle: SmartPLS Benutzerhandbuch,
2004, http://www.smartpls.de, retrieved on: February 15, 2006. SmartPLS is currently
available in version 2.0 beta.

650
Cf. Haenlein, Michael and Andreas M. Kaplan: A beginner's guide to partial least
squares analysis, in: Understanding Statistics, Vol. 3 (2004), No. 4, p. 291.

651
Cf. Chin and Newsted: Strutural equation modeling analysis with small samples using
partial least squares .

652
See Herrmann, Huber and Kressmann: Partial least squares - Ein Leitfaden zur
Spezifikation, Schätzung und Beurteilung varianzbasierter Strukturgleichungsmodelle,

p. 55.
653
Fornell and Bookstein: Two structural equation models: LISREL and PLS applied to
consumer exit-voice theory, p. 450.


Analysis of the Supply Chain Management Framework

based, it was not designed for this specific analysis. Therefore, it is likely that
there exists an inherent measurement error to some degree. Thirdly, though the
sample size is relatively large, it is by some not considered large enough as the
minimum sample size for a LISREL model.654

The issue of formative indicators is also of relevance in the SEM model under
consideration. The HPM database does not provide for an appropriate reflective
measure of integration. Therefore, the degree of ERP adoption has been chosen to
give an indication of integration among the sample. The way to operationalize
ERP adoption is to build a composite index that measures the degree of ERP
adoption.655 Another construct where it could be an issue would be the
performance construct. In the model under consideration, however, the
performance construct is of a more abstract nature that is assumed to explain
identified performance indicators. In such a design, performance indicators are of
reflective nature. This would be different if the performance indicators actually
defined the performance construct. In this case, the performance construct would
be a formative one.

654
For a more detailed discussion on sample sizes, cf. Marsh, Herbert W., John Balla and
Roderick P. McDonald: Goodness-of-fit indices in confirmatory factor analysis: Effects
of sample size, in: Psychological Bulleting, Vol. 103 (1988), pp. 391-411.

655
See sections C.III.2 and D.II.


Path Model Estimation Using Partial Least Squares (PLS)

Table D-1: Comparison of PLS and covariance-based SEMs (e.g., LISREL).

Criterion PLS Covariance-based SEM
(e.g., LISREL)
Objective Prediction oriented Parameter oriented
Approach Variance based Covariance based
Assumptions Predictor specification
(non parametric)
Typically multivariate
normal distribution and
independent observations
(parametric)
Parameter estimates Consistent as indicators
and sample size increase
(i.e., consistency at large)
Consistent
Latent variable scores Explicitly estimated Indeterminate
Epistemic relationship656
between a latent variable
and its measures
Can be modeled in either
formative or reflective
mode
Typically only with
reflective indicators
(however, procedures to
consider formative
indicators exist)657
Implications Optimal for prediction
accuracy
Optimal for parameter
accuracy
Model complexity Large complexity (e.g.,
100 constructs and 1000
indicators)
Small to moderate
complexity (e.g., less than
100 indicators)
Sample size Power analysis based on
the portion of the model
with the largest number
of predictors. Minimal
recommendations range
from 30 to 100 cases
Ideally based on power
analysis of specific model –
minimal recommendations
range from 100 to 800.658

656
Epistemic relationships refer to the nature of the links between constructs and
indicators, i.e., reflective or formative, cf. Hulland: Use of partial least squares (PLS) in
strategic management research: A review of four recent studies, p. 201.

657
See Herrmann, Huber and Kressmann: Partial least squares - Ein Leitfaden zur
Spezifikation, Schätzung und Beurteilung varianzbasierter Strukturgleichungsmodelle,
pp. 55-57.

658
Cf. Homburg and Hildebrandt: Die Kausalanalyse: Bestandsaufnahme, Entwicklungsrichtungen,
Problemfelder, p. 23 for the suggestion of 100 as the smallest sample
size, others suggest 200.


Analysis of the Supply Chain Management Framework

3.
Methodological Application and Interpretation of the Partial Least Squares
Method
In PLS, parameter estimation is not conducted simultaneously. Therefore, no
overall goodness-of-fit measures exist that assess the overall model fit, as it is the
case for covariance-based methods. In order to evaluate the PLS outcome and its
validity, several methods and procedures considering the different measurement
models should be applied as follows.

Generally, PLS models are analyzed and interpreted in two consecutive
steps.659 First, the reliability and validity of the measurement model is assessed
and then secondly, the structural model is assessed. By following this sequence, it
can be assured that reliable and valid measures of constructs are used before the
construct relationships are interpreted. Three methods for measurement model
assessment are available:660

1.
Individual item reliability examines the loadings of measures with their
respective construct. Generally, loadings higher than 0.7 are indicated.
However, often researchers find lower loadings. The ultimate threshold
researchers suggest varies between 0.4 and 0.5.661 The higher the measure
loadings, the lower the required number of indicators to explain a construct.662
In the case of formative indicators, the values correspond to simple
correlations with the construct and no loadings can be established.
2.
Convergent validity, also called composite reliability, measures the combined
construct validity. A commonly used reliability measure is Cronbach’s alpha,
where a value of 0.7 is considered to be a good threshold for composite
reliability.663 PLS, though, uses a slightly different approach to determine
composite reliability. Developed by Erts, Linn, and Jöresog, it does not
659
See Hulland: Use of partial least squares (PLS) in strategic management research: A
review of four recent studies, p. 198.

660
See Hulland: Use of partial least squares (PLS) in strategic management research: A
review of four recent studies, pp. 198-200; Amoroso and Cheney: Testing a causal
model of end-user application effectiveness, pp. 78-81.

661
Cf. Gammelgaard, Britta and Paul D. Larson: Logistics skills and competencies for
supply chain management, in: Journal of Business Logistics, Vol. 22 (2001), No. 2, p.
35; and Hair, Joseph F., Rolph E. Anderson and Ronald L. Tatham: Multivariate data
analysis, 2nd ed., New York 1987, p. 249.

662
Cf. Hulland: Use of partial least squares (PLS) in strategic management research: A
review of four recent studies, pp. 198-199.

663
See Nunnally, Jum C.: Psychometric theory, 2nd ed., New York 1978, p. 245; and
Hulland: Use of partial least squares (PLS) in strategic management research: A review
of four recent studies, p. 199. See also Cronbach, Lee J.: Coefficient alpha and the
internal structure of tests, in: Psychometrika, Vol. 16 (1951), pp. 297-334.


Path Model Estimation Using Partial Least Squares (PLS)

assume equally weighted indicators.664 Values of >0.8 are considered to show
a good composite reliability.

3.
Discriminant validity measures how indicators of one construct differ from the
indicators of other constructs in the same model, i.e. discriminate other
constructs. One criterion for discriminant validity is that the square root of
average variance explained by a construct should be greater than the
correlations among other constructs.665 The same can be achieved by
comparing the average variance explained with the square of correlations
between latent variables.666
The reliability and validity measures described above are only meaningful for
reflective indicators and not for formative indicators which do not have to be
correlated among each other. Instead, solid theory should be employed when
building a construct with formative indicators.

After the measurement model is assessed, the structural model can be
interpreted. As pointed out above, no proper overall goodness-of-fit measures
exist for PLS models because of a lack of simultaneous parameter estimation.667
As it is the objective of PLS to explain variances, it is important to report R2
values. The higher the reported R2 values, the better the model’s variance
explanation and therefore its predictive power. Researchers suggest different
values for R2 for a “good” variance explanation. According to Chin, a value of

0.67 is considered to be substantial, 0.33 average, and 0.19 weak.668
The change in R2 is used to assess the impact of a specific latent variable on
other latent variables and the effect size is measured by f 2:

664
Cf. Chin: The partial least squares approach to structural equation modeling, p. 320.
See also Werts, Charles E., Robert L. Linn and Karl G. Jöreskog: Interclass reliability
estimates: Testing structural assumptions, in: Educational and Psychological
Measurement, Vol. 34 (1974), pp. 25-33.

665
Cf. Hulland: Use of partial least squares (PLS) in strategic management research: A
review of four recent studies, pp. 199-200; and Chin: The partial least squares approach
to structural equation modeling, p. 321.

666
Cf. Fornell, Claes and David F. Larcker: Evaluating structural equation models with
unobservable variables and measurement error, in: Journal of Marketing Research, Vol.
18 (1981), No. 1, pp. 39-50; Chin: The partial least squares approach to structural
equation modeling , p. 321; and Hulland: Use of partial least squares (PLS) in strategic
management research: A review of four recent studies, p. 199

667
Cf. Herrmann, Huber and Kressmann: Partial least squares - Ein Leitfaden zur
Spezifikation, Schätzung und Beurteilung varianzbasierter Strukturgleichungsmodelle,

p. 58.
668
Cf. Chin and Newsted: Strutural equation modeling analysis with small samples using
partial least squares, p. 316.


Analysis of the Supply Chain Management Framework

R2 - R2

2 included excluded

[18]: f =

1- R2

included

By comparing R2 before and after exclusion of a latent variable, the effect can
be assessed. Values of 0.02, 0.15, and 0.35 for f 2 are considered to imply a small,
medium, or large effect at the structural level.669

The model can be interpreted based on the estimated path coefficients, which
indicate the direction of a relationship, i.e. negative, positive, or neutral. T-
statistics show the significance of these path coefficients. Lohmöller considers a
minimum path coefficient of 0.1, whereas Chin states significant path coefficient
values only above 0.2.670

PLS does not automatically report significance. Therefore, bootstrapping or
jackknifing procedures are used to determine the significance of the stated means
of path coefficients and weights. Comparing the two methods, bootstrapping
should be preferred because jackknifing can be considered to be an approximation
of the bootstrap procedure.671 Bootstrapping means that the model is estimated a
certain number of times (a standard iteration number is 100) with changing
fractions of the sample. Then, the resulting means of this procedure are compared
with the model results and tested for significance.

If a reflective model is analyzed, the predictive power can also be evaluated
with the Q2 Stone-Geisser test, which essentially examines how well the model
performs compared to performance by chance by using blindfolding procedures.
The larger the Q2 value, the better the models’ predictive power. Values below
zero indicate that the trivial prediction is better than the model equation prediction
and so results might be misleading. When noise predictors are removed from the
model, this might lead to an increase in Q2 values as well as significance, in
contrast to R2, which always decreases with such a deletion.672 It is differentiated
between a cross-validated communality Q2 and a cross-validated redundancy Q2 .
The latter is suggested if the predictive relevance of a theoretical, causal model is
examined or if prediction is made by those latent variables that predict a
dependent variable under consideration.673

669 Cf. Chin: The partial least squares approach to structural equation modeling , pp. 316


317.
670 Cf. Lohmöller: Latent path modeling with partial least squares, p. 60; and Chin: The
partial least squares approach to structural equation modeling, p. 324.
671 Cf. Chin: The partial least squares approach to structural equation modeling, p. 320.
672 Cf. Sellin, Norbert: Partial least squares modeling in research on educational

achievement, in: Bos, Wilfried and Rainer H. Lehmann (Eds.): Reflections on
educational achievement, Münster New York München Berlin 1995, pp. 262-263.
673 Cf. Chin: The partial least squares approach to structural equation modeling, pp. 317


318.

Path Model Estimation Using Partial Least Squares (PLS)

A procedure equivalent to that of the f 2 values to determine the impact of a
specific latent variable on R2 exists also for Q2. In order to determine the influence
of a latent variable on the predictive power of the model, q2 can be calculated in a

2 Q2

similar way as f . is determined excluding the latent variable under
consideration and compared to before Q2 exclusion, as shown in Equation [19].674

Q2 - Q2

2 included excluded

[19]: q =

1- Q2

included

In contrast to f 2 , q2 can also be negative values and in that case exclusion of a
latent variable can increase the predictive power of a model and therefore the
value for Q2. In this circumstance, such a latent variable would be considered to
add noise to the model.675

Using the procedures described above, it is possible to verify the validity and
meaning of PLS models fairly well. The PLS methodology can now be used in
order to verify the SCM framework developed in this text.

674 Cf. Chin: The partial least squares approach to structural equation modeling, pp. 317


318.
675 Cf. Chin: The partial least squares approach to structural equation modeling, p. 318.

Analysis of the Supply Chain Management Framework

II.
Supply Chain Management, E-Business Factors, and Performance for
the PLS Analysis
In order to derive a SEM to test the previously developed SCM framework, the
existing HPM database is used. As described before, the HPM project collects a
wide variety of data from each manufacturing plant. The scales have been
developed based on experiences of earlier data collections. However, they have
not been specifically designed for this framework. Therefore, compromises have
to be made in order to match available items and scales of the database with the
hypothesized model.676 Nevertheless, the scale identification is strictly theory
driven.

The major building blocks for the path model have been conceptualized before
as internal coordination, external coordination, internal collaboration, external
collaboration, and ERP integration.

Coordination and collaboration have been split into internal and external
components, as introduced in section C.II.3. Separating internal and external
coordination and collaboration is important because they represent distinct, but
nevertheless interdependent characteristics and their relationship towards each
other is of interest. Theoretically, one would assume that internal coordination and
collaboration are the basis for their external counterparts. Therefore, the assumed
causal relationship leads to arrows pointing from internal elements to external
elements. However, a study conducted by Stank, Keller, and Daugherty found that
external collaboration has no direct impact on performance but only an indirect
one through the element internal collaboration, which plays an intermediary role
in their analysis.677 Other studies confirm this finding and this will be tested in this
analysis as well.678

On the normative SCM framework level, customer orientation has been
identified as one of five meta-policies for SCM.679 This is accounted for in the
model through four items that measure the degree to which customers are

676 See Narasimhan, Ram and Jayanth Jayaram: Causal linkages in supply chain

management: An exploratory study of North American manufacturing firms, in:

Decision Sciences, Vol. 29 (1998), No. 3, pp. 588-589, and section D.III.3. for a

discussion of these issues with regard to the analysis at hand.
677 See Stank, Keller and Daugherty: Supply chain collaboration and logistical service

performance, pp. 38-39.
678 Cf. Sanders and Premus: Modeling the relationship between firm IT capability,

collaboration, and performance, pp. 14-15. Subrami implies that external

communication is constrained by internal communication processes, cf. Subramani:

How do suppliers benefit from information technology use in supply chain

relationships?, p. 52.
679 For a review, see section B.I.3.c and Appendix 1.


Supply Chain Management, E-Business Factors, and Performance

considered in the decision making process. Three items show factor loadings
greater than 0.7 and one item has a factor loading of 0.69. Cronbach’s alpha is

0.779 for this scale, with 61.33% explained variance.
Trust has been identified as the key SCM antecedent out of the ones listed in
the SCM framework.680 As trust is mainly of significance for partnerships, it is
assumed to have a positive impact on external coordination. Its relationship with
external collaboration could be seen in two ways. One is that trust is an important
prerequisite for external collaboration681 while on the other hand, as trust is
considered to build over time, external collaboration could be well a major
determinant for building trust in relationships.682 This latter view is taken in the
initial model.
Trust is measured by four items and all only refer to trust towards a plant’s
suppliers. Trust towards suppliers is more relevant because generally customers
are in a more powerful position and therefore tend to be accountable for the
characteristics of the relationship. If a company decides to have a trust-based
relationship with its suppliers, this reflects its attitude in this regard well.
Therefore, it is not seen as a problem that trust in customer relationships has not
been explicitly asked for separately. In the hypothesized model, a direct
connection between trust and customer satisfaction is established to examine its
possible impact. It is, however, rather unlikely to show a significant impact in
itself without mediating factors. Three indicators of trust have factor loadings
greater than 0.8 and one shows a factor loading of greater than 0.6. Cronbach’s
alpha is satisfactory with 0.771 and explained variance is 61.06%.
The factor ERP adoption is used as a rough approximation to determine the
degree of integration in the sample. It does not, however, clearly indicate the
degree to which a seamless information and material flow is accomplished.
Nevertheless, plants with a higher degree of ERP integration of their software
applications are considered to provide more support for seamless material and
information flows than those with a low adoption rate. This wider importance and
benefit of ERP integration in line with the purpose in the context of the analysis in
this text has been also suggested by Bendoly and Schoenherr. They base their
understanding of ERP integration on existing theories and confirm it through
statistical analyses and also suggest testing the impact outside their framework.
The SCM framework is well suitable for such an alternative environment.683

680
Cf. Appendix 1.

681
See Spekman, Kamauff Jr. and Myhr: An empirical investigation into supply chain
management: A perspective on partnerships, p. 66.

682
Cf. Rousseau et al.: Not so different after all: A cross-discipline view of trust, pp. 396


397.
683
Cf. Bendoly and Schoenherr: ERP system and implementation process benefits.
Implications for B2B e-procurement, pp. 316-317.


Analysis of the Supply Chain Management Framework

Other studies operationalize the use of IT or IT competency by reflective,
perceptual measures. For example, Sanders and Premus form a factor “firm IT
capability” to operationalize IT competency in companies.684 Since such measures
are not available in the HPM questionnaire, a composite score is determined. As
mentioned before, 19 application areas out of 31 have been chosen to represent
those applications that are relevant for SCM. This measure is operationalized in a
formative manner. As such, a composite score is established to determine the
degree of ERP integration. Each application that has been integrated in an ERP
system receives one point, so that a maximum of 19 points can be achieved. This
score determines the degree of overall ERP integration. Being a formative
measure, no reliability or validity measures can be calculated. Furthermore, the
composite score is used in the PLS model as one variable. Therefore, no further
analysis within the PLS model can be conducted but is also not necessary.

Based on these identified scales in the HPM database, a causal model is
proposed as depicted in Figure D-2. Performance is used as dependent variable in
the structural model. Customer satisfaction is selected as a performance measure.
This reflects not only the required customer focus in SCM, but also gives a more
encompassing picture of a manufacturing plant’s performance.

684
Cf. Sanders and Premus: Modeling the relationship between firm IT capability,
collaboration, and performance, pp. 8-10; and also see Kearns, Grover S. and Albert L.
Lederer: A resource-based view of strategic IT alignment: How knowledge sharing
creates competitive advantage, in: Decision Sciences, Vol. 34 (2003), No. 1, pp. 8-11.


Supply Chain Management, E-Business Factors, and Performance

CustomerSatisfactionCustomer
Satisfaction
InternalCollaborationInternal
Collaboration
ExternalCollaborationExternal
Collaboration
ExternalCoordinationExternal
Coordination
InternalCoordinationInternal
Coordination
CustomerOrientationCustomer
Orientation
ERPAdoptionERP
Adoption
TrustTrust
Figure D-2: Hypothesized causal SCM model, based on available HPM scales685

Many have pointed out the general positive link between customer satisfaction
and economic success.686 It is also a measure that reflects performance independently
from industry and plant size. Additionally, it can be assumed that if customer
satisfaction is high, overall supply chain performance is likely to satisfy customer
expectations up to the point of the customer’s customer. Obviously, if a
customer’s customer is not satisfied and this dissatisfaction can be traced back by
the immediate customer to bad performance of the supply chain examined in this

685
All latent variables but ERP integration are reflective measures. ERP integration is
formative.

686
See Homburg, Christian and Matthias Bucerius: Kundenzufriedenheit als
Managementherausforderung, in: Homburg, Christian (Ed.): Kundenzufriedenheit (5th
ed.), Wiesbaden 2003, pp. 63-66; Homburg, Christian and Bettina Rudolph: Customer
satisfaction in industrial markets: dimensional and multiple role issues, in: Journal of
Business Research, Vol. 52 (2001), No. 1, pp. 15-33; Lee, Hau L. and Corey Billington:
Managing supply chain inventory: Pitfalls and opportunities, in: Sloan Management
Review, Vol. 33 (1992), Spring, pp. 66-67; and Dresner, Martin and Kefeng Xu:
Customer service, customer satisfaction, and corporate performance in the service
sector, in: Journal of Business Logistics, Vol. 16 (1995), No. 1, p. 37.


Analysis of the Supply Chain Management Framework

analysis, customer satisfaction will be lower compared to better performing supply
chains. Consequently, it is better suited to reflect total supply chain performance
than measures that reflect only selected performances.

Although the HPM questionnaire was not explicitly designed for testing the
SCM framework, it has been possible to derive its key elements from the database
and these, represented by the identified scales, show satisfactory validity and item
and scale reliability. Therefore, they can be used for the causal model. Table D-2
shows all scales and their characteristics.687

Table D-2: Overview of reliability of reflective scales used in the causal model

Scale Cronbach’s
alpha
Variance
explained
Customer satisfaction 0.863 65.84%
Internal coordination 0.806 56.90%
External coordination 0.810 57.17%
Internal collaboration 0.893 70.18%
External collaboration 0.784 56.01%
Customer orientation 0.779 61.33%
Trust 0.771 61.06%

687 Details are provided in Appendix 3.


PLS Analysis of the Supply Chain Management Framework

III. PLS Analysis of the Supply Chain Management Framework
1. Path Model Estimation of Supply Chain Management Framework
After the identification of variables and the model structure, PLS is applied to
estimate path relationships. First, the entire hypothesized causal model structure,
as depicted in Figure D-2, is estimated. Table D-3 shows all resulting path
coefficients of the initial model. Based on this result, all insignificant links are
removed and the model is estimated again. The result of this second estimation
shows that all connections are significant at the p<0.05 level. The resulting model
structure with the new path coefficients and R2 are depicted in Figure D-3.

Table D-3: Path coefficients of initial model estimation

from
to
External
Collaboration
Internal
Collaboration Trust External
Coordination
Internal
Coordination
Customer
Orientation
ERP
Application
Performance 0.120* 0.312** 0.011 0.017 0.159** 0.252** 0.155**
External
Collaboration 0.296** 0.261** 0.366** -0.102*
Internal
Collaboration 0.456** -0.070
Trust 0.458** 0.178*
External
Coordination 0.422** 0.372** 0.166** 0.017
Internal
Coordination 0.243** -0.070

* significant at p<0.10
** significant, p<0.005
The first obviousness is the negative path coefficient between ERP adoption
and external collaboration. This suggests that a high ERP adoption rate indicates
lower external collaboration engagement. Such a result raises doubts about the
assumed complementary nature of ERP adoption and external collaborative
efforts. Nevertheless, the direct links from ERP adoption and external
collaboration to customer satisfaction show a positive impact for both elements.


Analysis of the Supply Chain Management Framework

0.296
0.367
0.167
CustomerSatisfactionCustomer
Satisfaction
InternalCollaborationInternal
Collaboration
ExternalCollaborationExternal
Collaboration
ExternalCoordinationExternal
Coordination
InternalCoordinationInternal
Coordination
CustomerOrientationCustomer
Orientation
ERPAdoptionERP
Adoption
TrustTrust
R2: 0.487
R2: 0.053
R2: 0.581
R2: 0.536
R2: 0.197
R2: 0.337
0.313
0.174
0.460
0.423
0.157
0.129
0.1700.373
0.256
0.443
0.262
-0.102
0.231
Figure D-3: Initial model path coefficients after removing insignificant links

Other obvious points are the low R2 values for internal coordination and
internal collaboration. Thus, following Stank, Keller, and Daugherty’s as well as
Sanders and Premus’ findings, the links are reversed and the model is estimated
again.688 As a result, both R2 values increase significantly while the reduction in R2
in external coordination and external collaboration decrease by a smaller amount.
Thus, the overall model fit increases. Furthermore, the path coefficients increase
significantly. This is an important outcome as it supports the findings of other
authors mentioned above who have identified this mediating function of internal
collaboration for external collaboration as well. As a side effect, path coefficients
from customer orientation decrease and the link between customer orientation and
internal coordination becomes insignificant. Figure D-4 shows the final model
estimation with all significant path connections.

688
Cf. Stank, Keller and Daugherty: Supply chain collaboration and logistical service
performance, pp. 14-15 and Sanders and Premus: Modeling the relationship between
firm IT capability, collaboration, and performance, pp. 38-19.


PLS Analysis of the Supply Chain Management Framework

0.490
0.458
0.173
CustomerSatisfactionCustomer
Satisfaction
InternalCollaborationInternal
Collaboration
ExternalCollaborationExternal
Collaboration
ExternalCoordinationExternal
Coordination
InternalCoordinationInternal
Coordination
CustomerOrientationCustomer
Orientation
ERPAdoptionERP
Adoption
TrustTrust
R2: 0.486
R2: 0.470
R2: 0.474
R2: 0.352
R2: 0.336
0.313
0.176
0.457
0.590
0.157
0.130
0.1690.602
0.256
0.152
0.362
-0.123
R2: 0.363

Figure D-4: Final PLS model configuration with best model fit for SCM framework

In another model run, not depicted here, it has been also tested what impact
reversing the link from external collaboration to trust has on the model estimation.
As assumed in the hypothesized model, the model estimation worsens with lower
variance explained within the model structure. Thus, the hypothesized causality is
supported and external collaboration seems to influence trust more positively than
vice versa.

A first indicator for evaluating the complete structural PLS model is the R2
value of the dependent latent variable as this reflects explained variance. The R2
value obtained for the dependent latent variable in the PLS model for the SCM
framework, i.e. performance in terms of customer satisfaction, is 0.486 or 48.6%
variance explained by the model, as can be observed in the model overview in
Figure D-4. Considering that the indicators and constructs have not been
developed for this specific analysis, this is a very good value as for such studies,
R2 is frequently lower.689

689
Cf. Narasimhan and Jayaram: Causal linkages in supply chain management: An
exploratory study of North American manufacturing firms, p. 597.


Analysis of the Supply Chain Management Framework

The precision of the PLS estimates can be measured through bootstrapping. As
a result, t-statistics provide the necessary information about the significance levels
of the construct linkages. Insignificant model paths have been already removed
from the model in Figure D-4. Table D-4 displays path coefficients and respective
significance levels, as they are also shown in Figure D-4.

Table D-4: Path coefficients of structural equation model

 from
to
External
Collaboration
Internal
Collaboration Trust External
Coordination
Internal
Coordination
Customer
Orientation
ERP
Application
Performance 0.130* 0.313** 0.169** 0.256** 0.157**
External
Collaboration 0.362** 0.458** -0.123*
Internal
Collaboration 0.490** 0.152*
Trust 0.457** 0.176*
External
Coordination 0.590** 0.174*
Internal
Coordination 0.602**

* significant, p<0.05 level
** significant, p<0.01 level
Overall, the model shows not only a high R2 value, but most of the postulated
relationships are also supported through significant path coefficients. In the next
section, the quality of the model estimation is examined and then, the findings are
discussed.

2. Quality, Validity, and Reliability of PLS Model Results
Convergent validity is calculated slightly differently in PLS compared to the
commonly used Cronbach’s alpha, though they are closely related. The difference
is that the PLS composite reliability measure considers item loadings obtained
within the causal model. Generally, Cronbach’s alpha is considered to be a
conservative measure, representing the lower bound estimate of reliability.690
Table D-5 compares the composite reliability of PLS with the Cronbach’s alphas
of the constructs and also shows the average variance explained. All values fulfill

690
Cf. Chin: The partial least squares approach to structural equation modeling , p. 320,
Hulland: Use of partial least squares (PLS) in strategic management research: A review
of four recent studies, p. 199; and Fornell and Larcker: Evaluating structural equation
models with unobservable variables and measurement error, pp. 39-50.


PLS Analysis of the Supply Chain Management Framework

the requirements of composite reliability greater than 0.8, Cronbach’s alpha
greater than 0.7, and average variance explained greater than 0.5.

Table D-5: Composite reliability, Cronbach’s alpha, and average variance explained for
reflective scales

Construct Composite
Reliability
Cronbach’s
alpha
Average Variance
explained
Customer satisfaction 0.905 0.863 0.657
External collaboration 0.862 0.784 0.561
Internal collaboration 0.921 0.893 0.701
Trust 0.860 0.771 0.610
External coordination 0.866 0.810 0.566
Internal coordination 0.866 0.806 0.564
Customer orientation 0.861 0.779 0.610

Discriminant validity assesses “[…] the extent to which measures of a given
construct differ from measures of other constructs in the same model.”691 Table D6
shows the correlation matrix for the model. It can be observed that all squared
correlation values are well below the average variance explained by each latent
variable. The results therefore confirm discriminant validity of the constructs.

691 Hulland: Use of partial least squares (PLS) in strategic management research: A review
of four recent studies, p. 199.


Analysis of the Supply Chain Management Framework

Table D-6: Assessing discriminant validity

Performance External
Collaboration
Internal
Collaboration Trust External
Coordination
Internal
Coordination
Customer
Orientation
Performance 0.657*
External
Collaboration 0.271** 0.562
Internal
Collaboration 0.332 0.336 0.701
Trust 0.174 0.312 0.168 0.610
External
Coordination 0.207 0.304 0.221 0.446 0.566
Internal
Coordination 0.162 0.121 0.184 0.204 0.362 0.566
Customer
Orientation 0.291 0.354 0.196 0.203 0.193 0.050 0.610

* Average Variance Explained (AVE) of constructs, bold numbers on diagonal
** Square of correlations between latent variables, numbers below diagonal
The predictive power of a model is assessed by Stone and Geisser’s Q2. Since
the developed PLS model represents a theoretical, causal model, the cross-
validated redundancy measure is chosen and a Q2 value of 0.236 is obtained, thus
implying that the model has predictive relevance.692

In order to evaluate the impact of a specific latent variable on the dependent
variable(s) in the structural model, f 2 values are calculated. Table D-7 shows the
f 2 values for all latent variables.

692 Cf. Chin: The partial least squares approach to structural equation modeling, p. 318.


PLS Analysis of the Supply Chain Management Framework

Table D-7: f 2 values for dependent latent variables

Performance External
Collaboration
Internal
Collaboration Trust External
Coordination
Internal
Coordination
Initial R-Square 0.486
External
Collaboration 0.012 0.239 0.199
Internal
Collaboration 0.101
Trust 0.000 0.504
External
Coordination 0.000 0.120 0.570
Internal
Coordination 0.041
Customer
Orientation 0.079 0.298 0.014 0.015 0.047
ERP adoption 0.041 0.025

Though some latent variables in the model show little to no impact on the
dependent variable performance, they do have an impact on other latent variables.
Trust, for example, shows no f 2 value for performance, but a strong value for
external coordination. Also, external coordination in itself shows no impact on
overall R2, but a rather high impact on internal coordination. Similarily, although
external collaboration demonstrates a significant relationship with performance, it
only displays a high f 2 value for internal collaboration, which in turn has a
medium impact on overall R2 .

It is noticeable that no single latent variable has a substantial impact on the
dependent variable performance, indicating that the model is relatively robust and
well-balanced and that not a single construct dominates the others.

As the impact of each latent variable on explained variance can be examined
by calculating the respective f 2 values, the same can be performed with regard to
Q2 . Table D-8 shows q2 values for all independent latent variables.


Analysis of the Supply Chain Management Framework

Table D-8: q2 values for all independent latent variables

Latent Variable q2 value
Initial Q2 0.236
External Collaboration 0.004
Internal Collaboration 0.072
Trust -0.001
External Coordination -0.001
Internal Coordination 0.027
Customer Orientation 0.045
ERP Application 0.025

It is evident from comparing Table D-7 with Table D-8 that both, f 2 and q2
values show the same implications. Latent variables that show a low influence on
explained variance in the ultimate dependent variable, i.e. performance, also have
a low to even slightly negative impact on the predictive power of the model.

In conclusion, internal collaboration and internal coordination exhibit an
impact on the overall model estimation, whereas external collaboration and
external coordination have a low impact or no impact whatsoever. Based on path
coefficients and f 2 values, however, external coordination and collaboration do
have a significant impact on their internal counterparts and are therefore still of
high importance. Furthermore, the model highlights the importance of customer
orientation, the relationship between external collaboration and trust, and the
direct impact of information system integration through ERP systems on
performance.

3.
Insights from and Limitations of the Empirical Investigation of the Supply
Chain Management Framework
After assessing the overall model, more detailed results can be interpreted. Several
outputs of PLS build the foundation for this. Path coefficients give an indication of
the extent of an effect between two latent variables. The higher the path
coefficient, the stronger the observed effect. Furthermore, f 2 and q2 values provide
additional information about the impact of latent variables on each other and on
the overall model estimation. This way, individual impacts can be better attributed
to a construct. Before beginning with this analysis, the usage of an existing
database for a newly developed framework is discussed because this renders some
difficulties for the definiteness of the obtained results.


PLS Analysis of the Supply Chain Management Framework

Several other studies have also used databases that were not specifically
designed for their research settings. In the field of operations management, two
studies can serve as an example. An analysis by Wathen that aims to examine the
relationship between production process focus and financial performance is based
on the Profit Impact on Marketing Strategies (PIMS) database, as are also several
studies in other research fields.693 A study conducted by Narasimhan and Jayaram
focused on the relationship among sourcing decisions, manufacturing goals,
customer responsiveness, and manufacturing performance using data from the
Global Manufacturing Research Group (GMRG) questionnaire II. In this analysis,
issues arising from the usage of available databases in research are addressed.694

In that context, Narasimhan and Jayaram identify four issues which are:

(1) unit of analysis and the associated frame of inference, (2) measurement issues,
(3) validity and generalizability issues, and (4) theory building via replication
studies.695 As a consequence, model results show lower values of explained
variance and overall reliability and validity tends to be lower. In the following, the
study at hand based on the HPM database is confronted with these issues in order
to determine the impact of limitations that might arise from the usage of this
specific database for testing the SCM framework.
The unit of analysis refers to problems that may arise when an existing
database includes data from a wide variety of industries, from different company
sizes, or from diverse products or markets. Consequently, it might be difficult to
establish valid comparisons and analyses. In the case of the HPM project, data
collection has been focused on three specific industries and the unit of analysis has
been the manufacturing business unit or plant level. Furthermore, six different
countries have been included so as to enable analyses of country-specific
differences. As a result, the unit of analysis and the purpose of analyzing SCM
matters match well.

Measurement issues arise if an available dataset does not provide specific
constructs for the object of analysis thus limiting the proper operationalization of
constructs. Therefore, available items have to be carefully screened and then
selected based on support provided through literature or logical considerations.

693 See Wathen, Samuel: Manufacturing strategy in business units, in: International Journal
of Operations & Production Management, Vol. 15 (1994), No. 8, p. 6.
694 See Narasimhan and Jayaram: Causal linkages in supply chain management: An
exploratory study of North American manufacturing firms, p. 595.

695
For a more detailed description of these four issues, cf. Narasimhan and Jayaram:
Causal linkages in supply chain management: An exploratory study of North American
manufacturing firms, p. 594. The following discussion is based on suggestions raised
by these authors, cf. Narasimhan and Jayaram: Causal linkages in supply chain
management: An exploratory study of North American manufacturing firms, pp. 594


597.

Analysis of the Supply Chain Management Framework

After constructs have been identified and designed, factor and reliability analysis
are used to evaluate the derived constructs. In the study at hand, this has been
carefully considered. Nevertheless, more accurate results could be gathered if
scales are developed for a specific research subject. The study conducted in this
text is hindered by the lack of an adequate scale reflecting supply chain integration
in the way it is intended in the context of the SCM core model, i.e. to measure to
what degree a seamless material and information flow is accomplished.

In terms of validity and accuracy, caution is indicated if data originates from
one single informant. It is suggested aiming for multi-informant responses in order
to mitigate this effect. Fortunately, this has been considered in the HPM project
and many items have been collected in the plants from more than one informant.

For the purpose of theory-building and theory-testing, as this is the aim of the
present analysis, different model designs should arrive at the same conclusions so
as to ensure convergence of findings. Convergence of findings is inherently
different from replication of findings because for convergence, different items and
scales are used. In contrast, replication uses an identical research design. Though
the SCM framework is unique, it builds on previously identified elements and
empirical findings. Furthermore, SEM supports previous findings within the new,
unique model structure, consequently contributing to the theory-building process
in SCM and operations management in general.

As a consequence of applying a theoretical model to an existing database,
explained variance measured by R2 is most likely lower compared to dedicated
studies and therefore findings should be interpreted according to model
specifications. However, this has not been experienced in the present study. R2
shows a rather high value indicating that the above mentioned concerns play a less
important role in this study. An explanation might be that the questionnaire design
considered issues such as single informant bias and that the questionnaires do have
a focus close to the subject under consideration. Nevertheless, the findings are still
to be interpreted in the context of model specifications. Although the lack of an
appropriate operationalization of SCM integration imposes a limitation of the
analysis, it sufficiently reflects ERP system integration, which in turn builds the
foundation for SCM integration systems. In light of this, reliability and validity of
the model are very good and therefore, a strong empirical support of the
hypothesized model of the SCM framework is clearly evident.

Comparing the first model estimation, depicted in Figure D-3, with the second,
shown in Figure D-4, the interdependence between internal and external
components becomes clear. As in previous studies, the model results suggest a
causal relationship from external coordination and collaboration to their respective
internal counterparts. The main positive performance impact emerges from the
degree of internal collaboration and internal coordination. Both external


PLS Analysis of the Supply Chain Management Framework

components, however, are shown to have a medium to high impact on their
internal counterparts.696

This suggests that internal practices benefit significantly from external
coordination and collaboration activities with customers and suppliers. If a higher
level of external coordination is implemented, it seems that internal coordination
will improve as a consequence. The same can be concluded with respect to
collaboration suggesting that it is beneficial for manufacturing plants to go ahead
with external initiatives because the organization will likely adapt internally
afterwards. Although there is strong support from external initiatives, not all
variance can be explained by them. It is important to note that additional efforts to
support internal coordination and collaboration are beneficial. Only if these two
are improved can a positive effect on performance be achieved. Besides the
positive effect of customer orientation on internal collaboration, no other such
efforts could be identified in this model.

External coordination shows no direct significant impact on performance. The
lack of the establishment of a direct connection between external coordination and
performance is reasonable because coordination relies more heavily on internal
communication than external collaboration relies on internal collaboration.
Without internal coordination, no overall positive effect can be achieved because
information communicated with external partners is pointless if it is not processed
internally as well. External collaboration, in contrast, shows a significant direct
effect on performance because it can also work to some degree without internal
collaboration. Its effect, however, is much weaker than that of internal
collaboration. In fact, internal collaboration shows the highest direct effect on
customer satisfaction and explains the highest portion of total variance, with a f 2
value of 0.101. Consequently, plants have to get their internal practices right,
especially internal collaboration practices as well as their external linkages. In
conclusion, external coordination and external collaboration are necessary and of
high importance but not sufficient to achieve superior performance.

In the model, the relationship between external collaboration, external
coordination, and trust becomes clear. External collaboration has a significant and
medium-sized effect on trust. This suggests that external collaboration contributes
to trust-building in relationships and this is in line with others who also suggest
such a positive impact on trust.697 Spekman, Kamauff Jr., and Myhr state that
collaboration requires a higher degree of trust than coordination, suggesting that

696 Effect size from weak to high are assumed from 0.1 on upwards, with 0.5 representing a

medium-sized effect. These are conservative numbers in light of the usage of an

existing database.
697 See Rousseau et al.: Not so different after all: A cross-discipline view of trust, pp. 396


397.

Analysis of the Supply Chain Management Framework

collaboration is a consequence of trust.698 In the model at hand, however, such a
link between trust and external collaboration is mediated by external coordination.

External coordination is strongly influenced by trust whereby a higher level of
trust enables more external coordination in relationships. Exchanging and sharing
information, process visibility, and availability of supply chain information in
general make information readily available although individual companies have
little to no control over the usage of this information. Therefore, a higher level of
trust is required and leads to a higher level of external coordination. External
coordination shows to have a medium size effect on external collaboration. If the
link between the two is reversed, this effect is not evident to this extent.

Thus, it can be concluded that engagement in external collaboration leads to
higher levels of trust, which in turn leads to higher levels of coordination in the
supply chain relationships examined. Such higher levels of coordinations then lead
to higher levels of collaboration. This triangle constellation suggests a positive
interdependence of these three latent variables. With a f 2 value of 0.199, external
collaboration shows a medium to high effect on explained variance of trust.
Customer orientation, as the second determinant of trust in the model, shows only
a weak effect on trust. Trust, in turn, indicates a high effect on the explained
variance of external coordination, with a f 2 value of 0.504. External coordination,
then, shows a medium effect on explained variance of external collaboration. With
a f 2 value of 0.120, this effect is the weakest within this triangle. Therefore,
external collaboration can be considered as the starting point for the positive
effects within this triangle.

The positive impact of ERP integration on customer satisfaction has already
been uncovered in section C.III.3. In the context of the overall SCM framework,
this direct effect is also supported. It is surprising, however, that a negative,
though weak, effect exists between the level of ERP integration and external
collaboration. One would assume that integration at least does not harm external
collaboration. The previous identification of SCM champions returned eleven
plants that display high ERP integration and high levels of SCM practice adoption.
Considering all plants, however, a weak but significant negative impact of ERP
integration on external collaboration capabilities exists. If SCM practices are not
developed to a high extent, ERP integration might lead to an increasingly internal
focus, thereby resulting in weaker external collaboration. ERP integration efforts
should therefore be accompanied by initiatives that prevent this increasing internal
focus so as to avoid its potential negative effect.

A customer oriented company culture mostly benefits the degree of external
collaboration. The other two determinants of external collaboration, ERP
integration and external coordination, explain less variance than customer

698
See Spekman, Kamauff Jr. and Myhr: An empirical investigation into supply chain
management: A perspective on partnerships, p. 56 and p. 66.


PLS Analysis of the Supply Chain Management Framework

orientation. This finding suggests that a high customer focus leads to higher levels
of external collaboration.

The multiple interrelations of customer orientation with other elements of the
framework underline the benefits of applying SEM and PLS. Insights such as the
previously described interdependencies among the various latent variables lead to
more differentiated implications. To a different extent, creating a customer
oriented mindset affects other areas of the company. Regarding customer
orientation practices, a weak to medium effect on customer satisfaction exists.
Customer oriented plants are indeed better equipped to meet customer
expectations. Nevertheless, internal collaboration has a stronger effect than
customer orientation. Therefore, customer orientation alone seems to be
insufficient and can be leveraged substantially by taking advantage of it for
establishing or improving external collaboration as well as internal collaboration,
external coordination, and trust.


E. Theoretical
and Practical Implications for
Successful Supply Chain Management
The concept of SCM has been comprehensively discussed in this text. Based on an
extensive literature review, a third generation SCM framework has been
developed that is believed to provide a structure for future research and
development of SCM. In doing so, the framework should contribute to organizing
the art of holistic and systemic SCM and to building a new basis for further
developments in the field.699

Often, the question is raised whether a concept remains or becomes just
another fad that will fade out after the hype.700 SCM has been identified as a
framework that comprises a philosophy with concrete, practical implications for
the competitiveness and efficiency of entire supply chains and its member
companies. Consequently, the SCM framework developed in this text is proposed
for providing the frame for further research on the different levels of SCM, i.e. the
normative, strategic, and operational levels. It is also a consequent development of
previous different management concept streams, such as quality management,
process reengineering, and customer orientation. Indisputably, all these concepts
have shown and still show an impact on competitiveness and efficiency of
companies.

As with marketing, it has been suggested considering SCM as an
organizational mindset that should be reflected throughout an organization and in
conjunction with other management approaches.701 The empirical analysis in this
text shows clear indications of the operational and strategic impact SCM practices
have on performance and competitiveness. This impact and the embracing scope
of SCM indicate that SCM is not a fad but a comprehensive management
framework with sustaining impact on organizational development.

One concern raised in the past has been that if taken too far, SCM might well
be considered as synonymous to management.702 Indeed, this is an important
remark as it points out the need to define the scope of SCM appropriately thus
preventing a loss of focus and precision. This has been considered in the third

699
The SCM framework follows therefore the evolutionary path of scientific development

outlined by Forrester, see Forrester: Industrial dynamics, p. 2.
700 See Chandra and Kumar: Supply chain management in theory and practice: A passing

fad or a fundamental change?, pp. 100-113; and Schroeder and Flynn: High

performance manufacturing: Just another fad?, pp. 3-17.
701 See Lambert, Cooper and Pagh: Supply chain management: Implementation issues and

research opportunities, p. 2.
702 Cf. Delfmann and Albers: Supply chain management in the global context, p. 5.


Implications for Successful Supply Chain Management

generation SCM framework developed in this text. By identifying a core SCM
model consisting of coordination, collaboration, and integration, linkages have
been clarified and the interfaces and interdependencies have become clear.

In the context of the overall analysis, several relevant SCM issues become
evident. Researchers should consider placing special emphasis on these which are:

-
Defining the scope of individual supply chains.

-
Considering interdependencies between different supply chains and the
associated network complexity. Often, such interdependencies between
different supply chains exist even within one organization.

-
Enabling a swift and even flow along a supply chain to improve
efficiency.703

-
Determining and managing power regimes in supply chains.

-
Identifying efficient performance frontiers in supply chains.

Within the discussion of SCM, several approaches have been proposed in this
text to address these issues. The definition of scope of supply chains appears to be
most appropriate along homogeneous product groups and according to the overall
value creation process, as outlined in section B.III.2.c. Once identified,
interdependencies and interferences between supply chains are easier to spot and
consequently also considered. Closely related to this is the determination of power
regimes in supply chains. Here, purely monetary considerations of value creation
might fall short. This is especially important when it comes to determining the
member(s) who predominately drive(s) the supply chain.704 Consequently, more
research into determinants of power and its implications for supply chains needs to
be conducted and related to the management of supply chains. A formal procedure
based on monetary considerations has been proposed in section B.IV.2.

The Theory of Swift, Even Flow is of high relevance for supply chain
improvement and competitiveness. Integration as one element of the core SCM
model together with process alignment initiatives and e-business capabilities have
been identified in this text as crucial enablers of a more swift and even flow along
supply chains.705 According to the theory, such a swift and even flow yields a
more efficient process. Being of technological nature, these enablers affect mainly
the efficient asset performance frontier. Managerial practices in coordination and
collaboration, in contrast, are likely to move the operating frontier even beyond

703 See Schmenner and Swink: On theory in operations management, pp. 102-103.
704 Cf. Walker: Unbundling the corporation: A blueprint for supply chain networks, p. 108;

and Mentzer et al.: Defining supply chain management, p. 14.
705 For some general remarks about what is beneficial for a swift and even flow, see

Schmenner and Swink: On theory in operations management, p. 104.


the asset frontier. In an ideal situation, both frontiers match and are therefore
balanced. Organizations are well advised to improve their competitive position
towards the efficient performance frontier by making use of available technologies
and management practices. The identification of both the efficient asset and the
efficient operating performance frontier is therefore subject to ongoing research by
monitoring company and competitor performance capabilities.

Several aspects of SCM have been clarified in this text. In the context of the
international research project High Performance Manufacturing, the link between
the core elements of the SCM model and strategic performance is empirically
supported. Important implications have been derived from the analysis by
applying PLS as an SEM method. The validity of the basic structure of the third
generation SCM framework is supported through the empirical data. This builds
therefore a solid foundation for future empirical research. Not all elements could
be conceptualized in the model, for example integration practices and a more
detailed e-business support. Consequently, this should be a focus of future
research.

In terms of integration, some conclusions can be derived. SCM champions
have been identified, i.e. plants that show high SCM practice adoption and high
ERP integration, and compared with SCM laggards, ERP savvy plants, and SCM
practice advocates. In addition, differences with regard to several performance
measures have been identified, and SCM champions show better performance in
almost all performance measures compared to the other groups. Significance,
however, could only be established for the measures customer satisfaction and
distinctive competencies. Future empirical research should therefore make an
effort to distinguish these groups more precisely and aim to establish significant
proof of performance differences.

For practitioners, the model estimation results yield valuable insights. First and
foremost, external practices in coordination and collaboration lead to higher
internal coordination and collaboration. Organizations are therefore implored to go
ahead with external supply chain initiatives because this seems to have a pull-
effect on internal practices. It is important, however, to additionally foster the
internal counterparts of coordination and collaboration and not only rely on this
pull-effect. Ultimate performance impact can only be established through more
intensive internal coordination and collaboration. External coordination and
collaboration have shown to have no or only a weak direct effect on performance
in the absence of their internal counterparts. Additionally, organizations should
consider prioritizing collaborative initiatives over coordinative initiatives as the
former proved to have a larger impact on performance. Furthermore, it has been
shown that external collaboration has a significant impact on trust. Higher levels
of trust lead to improved external coordination, followed by higher levels of
internal coordination and ultimately better overall performance.


Implications for Successful Supply Chain Management

Another practical implication is the recognition of customer orientation as a
foundation for better supply chain relationships and improved performance.
Customer orientation directly influences overall performance positively and
supports strongly external collaboration in supply chain relationships.

These findings support a positive overall impact of SCM. They, however, refer
mainly to strategic relationships with suppliers and customers where a higher level
of interdependency can be assumed. These findings should therefore be
considered for such relationships and according to the appropriateness of
relationships. Arm’s length relationships are likely to require different priorities.
Future research should therefore also investigate SCM practices according to the
power regimes of supply chain relationships. As most value of supply chains lies
in strategic relationships, however, the findings derived here are of high relevance
for supply chain competitiveness. In addition, they encourage organizations that
hold a powerful supply chain position to engage in cooperative supply chain
initiatives in the context of the SCM framework.


References

Ackoff, R. L.: From data to wisdom, in: Journal of Applied Systems Analysis,
Vol. 16 (1989), pp. 3-9.

Amit, Raphael and Christoph Zott: Value creation in e-business, in: Strategic
Management Journal, Vol. 22 (2001), No. 6/7, June/July, pp. 493-520.

Amoroso, Donald L. and Paul H. Cheney: Testing a causal model of end-user
application effectiveness, in: Journal of Management Information Systems, Vol.
8 (1991), No. 1, Summer, pp. 63-89.

Andraski, Joseph C.: Leadership and the realization of supply chain collaboration,
in: Journal of Business Logistics, Vol. 19 (1998), No. 2, pp. 9-11.

Backhaus, Klaus et al.: Multivariate Analysemethoden, 11th ed., Berlin
Heidelberg New York 2006.

Bakos, J. Yannis and Erik Brynjolfsson: Information technology, incentives, and
the optimal number of suppliers, in: Journal of Management Information
Systems, Vol. 10 (1993), No. 2, pp. 37-53.

Barclay, D.W., C. Higgins and R. Thompson: The partial least squares (PLS)
approach to causal modeling: Personal computer adaptation and use as an
illustration, in: Technology Studies, Vol. 2 (1995), No. 2, pp. 285-309.

Barney, Jay: Firm resources and sustained competitive advantage, in: Journal of
Management, Vol. 17 (1991), No. 1, March, pp. 99-120.

Barratt, Mark: Understanding the meaning of collaboration in the supply chain, in:
Supply Chain Management: An International Journal, Vol. 9 (2004), No. 1, pp.
30-42.

Basch, Michael D.: Harness the power of the Internet: A new model for the 21st
century, in: Information Executive, Vol. 4 (2000), No. 10, October, pp. 8-9.

Bask, Anu H. and Jari Juga: Semi-integrated supply chains: Towards the new era
of supply chain management, in: International Journal of Logistics: Research
and Applications, Vol. 4 (2001), No. 2, pp. 137-152.

Bechtel, Christian and Jayanth Jayaram: Supply chain management: A strategic
perspective, in: The International Journal of Logistics Management, Vol. 8
(1997), No. 1, pp. 15-34.

Bendoly, Elliot and Tobias Schoenherr: ERP system and implementation process
benefits. Implications for B2B e-procurement, in: International Journal of
Operations & Production Management, Vol. 25 (2005), No. 4, pp. 304-319.


232 References

Bharadwaj, Anandhi S.: A resource-based perspective on information technology
capability and firm performance: An empirical investigation, in: MIS Quarterly,
Vol. 24 (2000), No. 1, pp. 169-196.

Biggs, Maggie: E-commerce is hot today, but e-business is the gift that keeps on
giving all year long, in: Infoworld, Vol. 20 (1998), No. 21, p. 82.

Blalock, Hubert M.: Theory building and causal interferences, in: Blalock, Hubert

M. and Ann B. Blalock (Eds.): Methodology in social research, New York St.
Louis London 1968, pp. 155-198.
Bloch, Michael, Yves Pigneur and Arie Segev: On the road of electronic
commerce - a business value framework, gaining competitive advantage and
some research issues, 1996 (No. 1013), Berkeley,
http://groups.haas.berkeley.edu/citm/publications/papers/wp-1013.html,
retrieved on: July 17, 2006.

Boarman, Patrick M.: Antitrust laws in a global market, in: Challenge (1993),
January/February, pp. 30-36.

Bollen, Kenneth A.: Structural equations with latent variables, New York et al.
1989.

Bond, B. et al.: ERP is dead - long live ERP II, GartnerGroup, 2000.

Bortz, Jürgen: Statistik für Sozialwissenschaftler, 4th ed., Berlin Heidelberg New
York 1993.

Boulding, Kenneth E: General system theory - the skeleton of science, in:
Management Science, Vol. 2 (1956), pp. 197-208.

Bowersox, Donald J., David J. Closs and Theodore P. Stank: Ten mega-trends that
will revolutionize supply chain logistics, in: Journal of Business Logistics, Vol.
21 (2000), No. 2, pp. 1-15.

Bragg, Wayne: Executive commentary on Liedtka's article: Collaboration across
lines of business for competitive advantage, in: Academy of Management
Executive, Vol. 10 (1996), No. 2, pp. 35-36.

Brayton, Colin: Data visualization: A brief history, in: Securities Industry News,
Vol. 17, No. 25 2005, pp. 7-29.

Busch, Axel and Wilhelm Dangelmaier: Integriertes Supply Chain Management -
ein koordinationsorientierter Überblick, in: Busch, Axel and Wilhelm
Dangelmaier (Eds.): Integriertes Supply Chain Management: Theorie und
Praxis effektiver unternehmensübergreifender Geschäftsprozesse (2nd ed.),
Wiesbaden 2004, pp. 1-21.


Butaney, Gul and Lawrence H. Wortzel: Distributor power versus manufacturer
power: The customer role, in: Journal of Marketing, Vol. 52 (1988), January,
pp. 52-63.

Cachon, Gérard P. and Martin A. Lariviere: Capacity choice and allocation:
Strategic behavior and supply chain performance, in: Management Science,
Vol. 45 (1999), No. 8, pp. 1091-1108.

Cachon, Gérard P. and Marshall Fisher: Supply chain inventory management and
the value of shared information, in: Management Science, Vol. 46 (2000), No.
8, pp. 1032-1048.

Cagliano, Raffaella, Federico Caniato and Gianluca Spina: E-business strategy:
How companies are shaping their supply chain through the internet, in:
International Journal of Operations & Production Management, Vol. 23 (2003),
No. 10, pp. 1142-1162.

Carter, Joseph R. and Bruce G. Ferrin: The impact of transportation costs on
supply chain management, in: Journal of Business Logistics, Vol. 16 (1995),
No. 1, pp. 189-212.

Ceruzzi, Paul E.: Reckoners: The prehistory of the digital computer, Westport,
Connecticut 1983.

Ceruzzi, Paul E.: A history of modern computing, Cambridge, Massachusetts
1998.

Chandra, Charu and Sameer Kumar: Supply chain management in theory and
practice: A passing fad or a fundamental change? in: Industrial Management &
Data Systems, Vol. 100 (2000), No. 3, pp. 100-113.

Chen, Frank et al.: Quantifying the bullwhip effect in a simple supply chain: The
impact of forecasting, lead times, and information, in: Management Science,
Vol. 46 (2000), No. 3, March, pp. 436-443.

Chen, Injazz J. and Antony Paulraj: Towards a theory of supply chain
management: The constructs and measurements, in: Journal of Operations
Management, Vol. 22 (2004), pp. 119-150.

Chen, Injazz J. and Antony Paulraj: Understanding supply chain management:
Critical research and a theoretical framework, in: International Journal of
Production Research, Vol. 42 (2004), No. 1, pp. 131-163.

Chin, Wynne W.: The partial least squares approach to structural equation
modeling, in: Marcoulides, G. A. (Ed.): Modern methods for business research,
Mahwah 1998, pp. 295-358.


234 References

Chin, Wynne W. and Peter A. Todd: On the use, usefulness, and ease of use of
structural equation modeling in MIS research: A note of caution, in: MIS
Quarterly, Vol. 19 (1995), June, pp. 237-246.

Chin, Wynne W. and Peter R. Newsted: Strutural equation modeling analysis with
small samples using partial least squares, in: Hoyle, Rick H. (Ed.): Strategies
for small sample research, Thousand Oaks, CA 1999, pp. 307-341.

Chin, Wynne W., Barbara L. Marcolin and Peter R. Newsted: A partial least
squares latent variable modeling approach for measuring interaction effects:
Results from a Monte Carlo simulation study and an electronic-mail emotion /
adoption study, in: Information Systems Research, Vol. 14 (2003), No. 2, June,
pp. 189-217.

Chmielewicz, Klaus: Forschungskonzeptionen der Wirtschaftswissenschaft, 3rd
ed., Stuttgart 1994.

Chopra, Sunil and Jan A. Van Mieghem: Which e-business is right for your supply
chain? in: Supply Chain Management Review (2000), July/August, pp. 32-40.

Chopra, Sunil and Peter Meindl: Supply chain management: Strategy, planning,
and operation, 2nd ed., Upper Saddle River 2004.

Christopher, Martin: Logistics and supply chain management, London 1992.

Christopher, Martin: Logistics and supply chain management: Creating value-
adding networks, 3rd ed., Harlow 2005.

Clemons, Eric K., Sashidhar P. Reddi and Michael C. Row: The impact of
information technology on the organization of economic activity: The 'move to
the middle' hypothesis, in: Journal of Management Information Systems, Vol.
10 (1993), No. 2, pp. 9-35.

Cleveland, Harland: Information as resource, in: The Futurist (1982), December,
pp. 34-39.

Coase, Ronald H.: The nature of the firm, in: Economica, Vol. 4 (1937), No. 1316,
pp. 386-405.

Cooper, Martha C., Douglas M. Lambert and Janus D. Pagh: Supply chain
management: More than a new name for logistics, in: The International Journal
of Logistics Management, Vol. 8 (1997), No. 1, pp. 1-14.

Cousins, Paul D.: The alignment of appropriate firm and supply strategies for
competitive advantage, in: International Journal of Operations & Production
Management, Vol. 25 (2005), No. 5, pp. 403-428.

Cox, Andrew: Power, value and supply chain management, in: Supply Chain
Management: An International Journal, Vol. 4 (1999), No. 4, pp. 167-175.


Cox, Andrew: The power perspective in procurement and supply management, in:
The Journal of Supply Chain Management, Vol. 37 (2001), No. 2, Spring, pp. 4


7.
Cox, Andrew: The art of the possible: Relationship management in power regimes
and supply chains, in: Supply Chain Management: An International Journal,
Vol. 9 (2004), No. 5, pp. 346-356.

Cox, Andrew: Business relationships for competitive advantage, Basingstoke
2004.

Cronbach, Lee J.: Coefficient alpha and the internal structure of tests, in:
Psychometrika, Vol. 16 (1951), pp. 297-334.

Croom, Simon R.: The impact of e-business on supply chain management, in:
International Journal of Operations & Production Management, Vol. 25 (2005),
No. 1, pp. 55-73.

Cross, Gary J.: How e-business is transforming supply chain management, in:
Journal of Business Strategy (2000), March/April, pp. 36-39.

Davenport, Thomas H.: Process innovation, Boston 1993.

Davenport, Thomas H. and Laurence Prusak: Working knowledge: How
organizations manage what they know, Boston, MA 1998.

Delfmann, Werner and Sascha Albers: Supply chain management in the global
context, 2000, Working Paper Series of the Seminar für allgemeine
Betriebswirtschaftslehre, betriebswirtschaftliche Planung und Logistik (No.
102), Cologne.

Dess, Gregory G. and Richard B. Robinson Jr.: Measuring organizational
performance in the absence of objective measures: The case of the privately-
held firm and conglomerate business unit, in: Strategic Management Journal,
Vol. 5 (1984), July-September, pp. 265-273.

Devaraj, Sarv, David G. Hollingworth and Roger G. Schroeder: Generic
manufacturing strategies and plant performance, in: Journal of Operations
Management, Vol. 22 (2004), pp. 313-333.

Diamantopoulos, Adamantios and Heidi M. Winklhofer: Index construction with
formative indicators: An alternative to scale development, in: Journal of
Marketing Research, Vol. 38 (2001), No. 2, pp. 269-277.

Dolmetsch, Ralph: eProcurement, München 2000.

Dresner, Martin and Kefeng Xu: Customer service, customer satisfaction, and
corporate performance in the service sector, in: Journal of Business Logistics,
Vol. 16 (1995), No. 1, pp. 23-40.


236 References

Drucker, Peter F.: The coming of the new organization, in: Harvard Business
Review, Vol. 66 (1988), January/February.

Duè, Richard T.: The value of information, in: Information Systems Management,
Vol. 13 (1996), No. 1, Winter, pp. 68-72.

Duffy, Richard and Andrew Fearne: The impact of supply chain partnerships on
supplier performance, in: The International Journal of Logistics Management,
Vol. 15 (2004), No. 1, pp. 57-71.

Dyer, Jeffrey H. and Harbir Singh: The relational view: Cooperative strategy and
sources of interorganizational competitive advantage, in: Academy of
Management Review, Vol. 23 (1998), No. 4, pp. 660-679.

Earl, M. J.: Management strategies for information technology, Englewood Cliffs
1989.

Emery, Frederick E. and Eric L. Trist: The causal texture of organizational
environments, in: Human Relations, Vol. 18 (1965), No. 1, January, pp. 21-32.

Eppen, Gary D.: Effects of centralization on expected costs in a multi-location
newsboy problem, in: Management Science, Vol. 25 (1979), No. 5, pp. 498


501.
Evans, Philip and Thomas S. Wurster: Blown to bits, Boston, Massachusetts 2000.

Evans, Philip B. and Thomas S. Wurster: Strategy and the new economics of
information, in: Harvard Business Review, Vol. 75 (1997), September/October,
pp. 71-82.

Ferdows, Kasra, Michael Lewis and Jose A. D. Machuca: Rapid-fire fulfillment,
in: Harvard Business Review, Vol. 82 (2004), No. 11, November, pp. 104-110.

Field, Alan M.: Think it through, in: The Journal of Commerce (2005), January
24, 2005, pp. 34-37.

Field, Andy: Discovering statistics using SPSS, 2nd ed., London 2005.

Fisher, Marshall L.: What is the right supply chain for your product? in: Harvard
Business Review, Vol. 75 (1997), March-April, pp. 105-116.

Fitzgibbons, Daniel, Lawrence Hutter and Scott Sopher: Radio frequency
identification (RFID): Critical considerations for manufacturers, Deloitte &
Touche USA LLP, 2004.

Fleischmann, Bernhard, Herbert Meyr and Michael Wagner: Advanced Planning,
in: Stadtler, Hartmut and Christoph Kilger (Eds.): Supply chain management
and advanced planning: concepts, models, software and case studies (2nd ed.),
Berlin Heidelberg New York 2002, pp. 71-96.


Flynn, Barbara B., Roger G. Schroeder and Sadao Sakakibara: A framework for
quality management research and an associated measurement instrument, in:
Journal of Operations Management, Vol. 11 (1994), pp. 339-366.

Flynn, Barbara B. et al.: World class manufacturing project - overview and
selected results, in: International Journal of Operations & Production
Management, Vol. 17 (1997), No. 7, pp. 671-685.

Fornell, Claes and David F. Larcker: Evaluating structural equation models with
unobservable variables and measurement error, in: Journal of Marketing
Research, Vol. 18 (1981), No. 1, pp. 39-50.

Fornell, Claes and Fred L. Bookstein: Two structural equation models: LISREL
and PLS applied to consumer exit-voice theory, in: Journal of Marketing
Research, Vol. 19 (1982), No. 4, November, pp. 440-452.

Fornell, Claes and Jaesung Cha: Partial least squares, in: Bagozzi, Richard P.
(Ed.): Advanced methods of marketing research, Cambridge, MA 1994, pp. 52


78.
Forrester, Jay W.: Industrial dynamics: A major breakthrough for decision makers,
in: Harvard Business Review, Vol. 38 (1958), July/August, pp. 37-66.

Forrester, Jay W.: Industrial dynamics, Waltham 1961, reprinted in 1999.

Forrester, Jay W.: Industrial dynamics - after the first decade, in: Management
Science, Vol. 14 (1968), No. 7, pp. 398-415.

Forrester, Jay W.: Principles of systems, second preliminary edition, 2nd ed.,
Cambridge, Massachusetts 1969.

Fritz, Wolfgang: Internet-Marketing und Electronic Commerce, 3rd ed.,
Wiesbaden 2004.

Frohlich, Markham T.: E-integration in the supply chain: Barriers and
performance, in: Decision Sciences, Vol. 33 (2002), No. 4, pp. 537-556.

Frohlich, Markham T. and Roy Westbrook: Arcs of integration: An international
study of supply chain strategies, in: Journal of Operations Management, Vol. 19
(2001), pp. 185-200.

Frohlich, Markham T. and Roy Westbrook: Demand chain management in
manufacturing and services: Web-based integration, drivers and performance,
in: Journal of Operations Management, Vol. 20 (2002), pp. 729-745.

Fudenberg, Drew and Jean Tirole: Understanding rent dissipation: On the use of
game theory in industrial organization, in: The American Economic Review,
Vol. 77 (1987), No. 2, May, pp. 176-183.


238 References

Fulkerson, William: Information-based manufacturing in the informational age, in:
The International Journal of Flexible Manufacturing Systems, Vol. 12 (2000),
No. 2/3, April, pp. 131-143.

Gabszewicz, Jean J. and Jacques-Francois Thisse: Location, in: Aumann, Robert J.
and Sergiu Hart (Eds.): Handbook of game theory with economic applications
volume 2, Amsterdam 1994.

Gammelgaard, Britta and Paul D. Larson: Logistics skills and competencies for
supply chain management, in: Journal of Business Logistics, Vol. 22 (2001),
No. 2, pp. 27-50.

Geary, Steve, Paul Childerhouse and Denis R. Towill: Uncertainty and the
seamless supply chain, in: Supply Chain Management Review (2002),
July/August, pp. 52-61.

Giannakis, Mihalis and Simon R. Croom: Toward the development of a supply
chain management paradigm: A conceptual framework, in: Journal of Supply
Chain Management, Vol. 40 (2004), No. 2, Spring, pp. 27-37.

Gitman, Lawrence J.: Principles of managerial finance, 11th ed., Boston 2006.

Göpfert, Ingrid: Einführung, Abgrenzung und Weiterentwicklung des Supply
Chain Managements, in: Busch, Axel and Wilhelm Dangelmaier (Eds.):
Integriertes Supply Chain Management: Theorie und Praxis effektiver
unternehmensübergreifender Geschäftsprozesse (2nd ed.), Wiesbaden 2004, pp.
25-45.

Gosain, Sanjay, Arvind Malhotra and Omar A. El Sawy: Coordinating for
flexibility in e-business supply chains, in: Journal of Management Information
Systems, Vol. 21 (2004), No. 3, Winter, pp. 7-45.

Gutenberg, Erich: Grundlagen der Betriebswirtschaftslehre. Band I: Die
Produktion, 24th ed., Berlin Heidelberg New York 1983.

Haenlein, Michael and Andreas M. Kaplan: A beginner's guide to partial least
squares analysis, in: Understanding Statistics, Vol. 3 (2004), No. 4, pp. 283


297.
Hagel III, John and Marc Singer: Unbundling the corporation, in: Harvard
Business Review, Vol. 77 (1999), March/April, pp. 133-141.

Hair, Joseph F., Rolph E. Anderson and Ronald L. Tatham: Multivariate data
analysis, 2nd ed., New York 1987.

Hammer, Michael: Reengineering work: Don't automate, obliterate, in: Harvard
Business Review, Vol. 68 (1990), July/August, pp. 104-112.


Hammer, Michael: The superefficient company, in: Harvard Business Review,
Vol. 79 (2001), September, pp. 82-91.

Hansmann, Karl-Werner and Christian M. Ringle: SmartPLS Benutzerhandbuch,
2004, http://www.smartpls.de, retrieved on: July 17, 2006.

Harland, C. M.: Supply chain management: Relationships, chains and networks,
in: British Journal of Management, Vol. 7 (1996), Special Issue, pp. S63-S80.

Hayes, Robert H. and Steven C. Wheelwright: Restoring our competitive edge competing
through manufacturing, New York 1984.

Hayes, Robert H. and Gary P. Pisano: Beyond world-class: The new
manufacturing strategy, in: Harvard Business Review, Vol. 72 (1994),
January/February, pp. 77-86.

Heires, Katherine and Ajit Kambil: Tracking RFID's next wave to gain strategic
advantage, 2004.

Heringer, Crispin: Qualitätsmanagement, in: Wannenwetsch, Helmut H. (Ed.):
Vernetztes Supply Chain Management, Berlin Heidelberg New York 2005, pp.
363-380.

Herrmann, Andreas, Frank Huber and Frank Kressmann: Partial least squares -
Ein Leitfaden zur Spezifikation, Schätzung und Beurteilung varianzbasierter
Strukturgleichungsmodelle, forthcoming in: Zeitschrift für betriebswirtschaftliche
Forschung, Vol. 58 (2006), No. 2, pp. 34-66.

Hertz, Susanne: Dynamics of alliances in highly integrated supply chain networks,
in: International Journal of Logistics: Research and Applications, Vol. 4 (2001),
No. 2, pp. 238-256.

Hildebrandt, Lutz: Kausalanalytische Validierung in der Marketingforschung, in:
Hildebrandt, Lutz and Christian Homburg (Eds.): Die Kausalanalyse, Stuttgart
1998, pp. 86-110.

Hill, Terry: Manufacturing strategy, London 1985.

Hill, Terry: Operations management, 2nd ed., New York 2005.

Hobbes, Robert: Hobbes' Internet timeline, version 8.1, 2005,
http://www.zakon.org/robert/internet/timeline/, retrieved on: July 17, 2006.

Hoffmann, Christoph: Logistik und Electronic Business, Wiesbaden 2001.

Homburg, Christian and Hans Baumgartner: Beurteilung von Kausalmodellen -
Bestandsaufnahme und Anwendungsempfehlungen, in: Marketing - Zeitschrift
für Forschung und Praxis, Vol. 17 (1995), No. 3, pp. 162-176.


240 References

Homburg, Christian and Lutz Hildebrandt: Die Kausalanalyse: Bestandsaufnahme,
Entwicklungsrichtungen, Problemfelder, in: Hildebrandt, Lutz and Christian
Homburg (Eds.): Die Kausalanalyse: Instrumente der empirischen
betriebswirtschaftlichen Forschung, Stuttgart 1998, pp. 15-44.

Homburg, Christian and Bettina Rudolph: Customer satisfaction in industrial
markets: dimensional and multiple role issues, in: Journal of Business Research,
Vol. 52 (2001), No. 1, pp. 15-33.

Homburg, Christian and Matthias Bucerius: Kundenzufriedenheit als
Managementherausforderung, in: Homburg, Christian (Ed.):
Kundenzufriedenheit (5th ed.), Wiesbaden 2003, p. 613.

Homburg, Christian, John P. Workman and Harley Krohmer: Marketing's
influence within the firm, in: Journal of Marketing, Vol. 63 (1999), No. 4,
April, pp. 1-17.

Horvath, Laura: Collaboration: The key to value creation in supply chain
management, in: Supply Chain Management: An International Journal, Vol. 6
(2001), No. 5, pp. 205-207.

Houlihan, John B.: International supply chain management, in: International
Journal of Physical Distribution & Materials Management, Vol. 15 (1985), No.
1, pp. 22-38.

Hoyt, James and Faizul Huq: From arms-length to collaborative relationships in
the supply chain: an evolutionary process, in: International Journal of Physical
Distribution & Logistics Management, Vol. 30 (2000), No. 9, pp. 750-764.

Huan, Samuel H., Sunil K. Sheoran and Ge Wang: A review and analysis of
supply chain operations reference (SCOR) model, in: Supply Chain
Management: An International Journal, Vol. 9 (2004), No. 1, pp. 23-29.

Hulland, John: Use of partial least squares (PLS) in strategic management
research: A review of four recent studies, in: Strategic Management Journal,
Vol. 20 (1999), No. 2, pp. 195-204.

Illgner, Elke: Grundlagen und Anwendungen der Internettechnologie im SCM, in:
Wannenwetsch, Helmut H. (Ed.): Vernetztes Supply Chain Management, Berlin
Heidelberg New York 2005, pp. 29-56.

Illgner, Elke: Praxisinstrumente für eine erfolgreiche SCM-Realisierung, in:
Wannenwetsch, Helmut H. (Ed.): Vernetztes Supply Chain Management, Berlin
Heidelberg New York 2005, pp. 81-95.

Ittner, Christopher D. and David F. Larcker: The performance effects of process
management techniques, in: Management Science, Vol. 43 (1997), No. 4, April,
pp. 522-534.


Johnson, M. Eric and Seungjin Whang: E-business and supply chain management:
An overview and framework, in: Production and Operations Management, Vol.
11 (2002), No. 4, pp. 413-423.

Kahl, Steven J. and Thomas P. Berquist: A primer on the internet supply chain, in:
Supply Chain Management Review (2000), September/October, pp. 41-48.

Kahn, Kenneth B. and John T. Mentzer: Logistics and interdepartmental
integration, in: International Journal of Physical Distribution & Logistics
Management, Vol. 26 (1996), No. 8, pp. 6-14.

Kaluza, Bernd and Guido Klenter: Zeit als strategischer Erfolgsfaktor von
Industrieunternehmen, 1992, Diskussionsbeiträge des Fachbereichs
Wirtschaftswissenschaften der Universität - Gesamthochschule - Duisburg (No.
173), Duisburg.

Kanji, Gopal K. and Alfred Wong: Business excellence model for supply chain
management, in: Total Quality Management, Vol. 10 (1999), No. 8, pp. 11471168.


Kaplan, Robert S. and David P. Norton: Linking the balanced scorecard to
strategy, in: California Management Review, Vol. 39 (1996), No. 1, pp. 53-79.

Katz, Michael L. and Carl Shapiro: Network externalities, competition, and
compatibility, in: American Economic Review, Vol. 75 (1985), pp. 424-440.

Kearns, Grover S. and Albert L. Lederer: A resource-based view of strategic IT
alignment: How knowledge sharing creates competitive advantage, in: Decision
Sciences, Vol. 34 (2003), No. 1, pp. 1-29.

Ketokivi, Mikko A. and Roger G. Schroeder: Perceptual measures of
performance: Fact or fiction? in: Journal of Operations Management, Vol. 22
(2004), pp. 247-264.

Kilger, Christoph and Boris Reuter: Collaborative planning, in: Stadtler, Hartmut
and Christoph Kilger (Eds.): Supply chain management and advanced planning:
Concepts, models, software and case studies (2nd ed.), Berlin Heidelberg New
York 2002, pp. 223-237.

Kim, Soo Wook and Ram Narasimhan: Information system utilization in supply
chain integration efforts, in: International Journal of Production Research, Vol.
40 (2002), No. 18, pp. 4585-4609.

Kleinrock, Leonard: Information flow in large communication nets, in: RLE
Quarterly Progress Report (1961), July.

Knolmayer, Gerhard, Peter Mertens and Alexander Zeier: Supply chain
management based on SAP systems, Berlin Heidelberg New York 2002.


242 References

Kotler, Philip and Kevin Keller: Marketing management, 12th ed., Upper Saddle
River 2006.

Koudal, Peter: Mastering complexity in global manufacturing: Powering profits
and growth through value chain synchronization, Deloitte & Touche LLP, 2003.

Krajewski, Lee J. and Larry P. Ritzman: Operations management, 7th ed., Upper
Saddle River 2005.

Kuhn, Axel and Bernd Hellingrath: Supply Chain Management: Optimierte
Zusammenarbeit in der Wertschöpfungskette, Berlin Heidelberg New York
2002.

Lambert, Douglas M., Martha C. Cooper and Janus D. Pagh: Supply chain
management: Implementation issues and research opportunities, in: The
International Journal of Logistics Management, Vol. 9 (1998), No. 2, pp. 1-19.

Langemann, Timo: Collaborative Supply Chain Management, in: Busch, Axel and
Wilhelm Dangelmaier (Eds.): Integriertes Supply Chain Management: Theorie
und Praxis effektiver unternehmensübergreifender Geschäftsprozesse (2nd ed.),
Wiesbaden 2004, pp. 437-451.

Lee, Hau L.: Letters to the editor, in: Harvard Business Review, Vol. 75 (1997),
May/June, pp. 190-192.

Lee, Hau L.: Creating value through supply chain integration, in: Supply Chain
Management Review (2000), September/ October, pp. 30-36.

Lee, Hau L.: The triple-a supply chain, in: Harvard Business Review, Vol. 82
(2004), October, pp. 102-112.

Lee, Hau L. and Corey Billington: Managing supply chain inventory: Pitfalls and
opportunities, in: Sloan Management Review, Vol. 33 (1992), Spring, pp. 65


73.
Lee, Hau L. and Seungjin Whang: Information sharing in a supply chain, in:
International Journal of Technology Management, Vol. 20 (2000), No. 3/4, pp.
373-387.

Lee, Hau L. and Seungjin Whang: E-business and supply chain integration, 2001,
Stanford Global Supply Chain Management Forum.

Lee, Hau L., V. Padmanabhan and Seungjin Whang: The bullwhip effect in supply
chains, in: Sloan Management Review, Vol. 38 (1997), No. 3, Spring, pp. 93


102.
Lee, Hau L., V. Padmanabhan and Seungjin Whang: Information distortion in a
supply chain: The bullwhip effect, in: Management Science, Vol. 43 (1997),
No. 4, April, pp. 546-558.


Leiner, Barry M. et al.: A brief history of the Internet, 2003,
http://www.isoc.org/internet/history/brief.shtml, retrieved on: July 17, 2006.

Lewis, Ira and Alexander Talalayevsky: Logistics and information technology: A
coordination perspective, in: Journal of Business Logistics, Vol. 18 (1997), No.
1, pp. 141-157.

Li, Gang et al.: Comparative analysis on value of information sharing in supply
chains, in: Supply Chain Management: An International Journal, Vol. 10
(2005), No. 1, pp. 34-46.

Liedtka, Jeanne M.: Collaborating across lines of business for competitive
advantage, in: Academy of Management Executive, Vol. 10 (1996), No. 2, pp.
20-37.

Liker, Jeffrey K. and Thomas Y. Choi: Building deep supplier relationships, in:
Harvard Business Review, Vol. 82 (2004), No. 12, December, pp. 104-113.

Lohmöller, Jan-Bernd: Latent path modeling with partial least squares, Heidelberg
1989.

Lopez, David et al.: Impact of information technology and e-commerce on supply
chain management: Survey evidence from manufacturing companies in
Michigan, in: Journal of E-Business, Vol. 4 (2004), No. 1, June.

Maier, Frank: Die Integration wissens- und modellbasierter Konzepte zur
Entscheidungsunterstützung im Innovationsmanagement, Berlin 1995.

Marsh, Herbert W., John Balla and Roderick P. McDonald: Goodness-of-fit
indices in confirmatory factor analysis: Effects of sample size, in: Psychological
Bulleting, Vol. 103 (1988), pp. 391-411.

Mason-Jones, R. and D. R. Towill: Information enrichment: designing the supply
chain for competitive advantage, in: Supply Chain Management: An
International Journal, Vol. 2 (1997), No. 4, pp. 137-148.

McAfee, Andrew: The impact of enterprise information technology adoption on
operational performance: An empirical investigation, in: Production and
Operations Management, Vol. 11 (2002), No. 1, Spring, pp. 33-53.

McAfee, Andrew: Do you have too much IT? in: MIT Sloan Management Review
(2004), Spring, pp. 18-22.

McCormack, Kevin and Bill Johnson: Business process orientation, supply chain
management, and the e-corporation, in: IIE Solutions (2001), October, pp. 33


37.

244 References

McCormack, Kevin P. and William C. Johnson: Supply chain networks and
business process orientation: Advanced strategies and best practices, Boca
Raton 2003.

McCullen, Peter and Denis R. Towill: Diagnosis and reduction of bullwhip in
supply chains, in: Supply Chain Management: An International Journal, Vol. 7
(2002), No. 3, pp. 164-179.

Mellios, George G.: Logistics management: What, why, how, in: Journal of
Business Logistics, Vol. 5 (1984), No. 2, pp. 106-122.

Mentzer, John T. et al.: Defining supply chain management, in: Journal of
Business Logistics, Vol. 22 (2001), No. 2, pp. 1-25.

Meyr, Herbert, Jens Rohde and Hartmut Stadtler: Basics for modelling, in:
Stadtler, Hartmut and Christoph Kilger (Eds.): Supply chain management and
advanced planning: concepts, models, software and case studies (2nd ed.),
Berlin Heidelberg New York 2002, pp. 45-70.

Miller, Holmes: The multiple dimensions of information quality, in: Information
Systems Management, Vol. 13 (1996), No. 2, Spring, pp. 79-82.

Milling, Peter: Systemtheoretische Grundlagen zur Planung der
Unternehmenspolitik, Berlin 1981.

Milling, Peter: Informationstechnologie als Wettbewerbsfaktor industrieller
Unternehmen, 1986, Beiträge des Fachbereichs Wirtschaftswissenschaften der
Universität Osnabrück (No. 8614), Osnabrück, 15 pgs.

Milling, Peter: Die 'Fabrik der Zukunft' in strategischer Perspektive, in: Milling,
Peter and Günther Zäpfel (Eds.): Betriebswirtschaftliche Grundlagen moderner
Produktionsstrukturen, Berlin 1993, pp. 9-19.

Milling, Peter: Kybernetische Überlegungen beim Entscheiden in komplexen
Systemen, in: Milling, Peter (Ed.): Entscheiden in komplexen Systemen, Berlin
2002, pp. 11-26.

Min, Soonhong and John T. Mentzer: Developing and measuring supply chain
management concepts, in: Journal of Business Logistics, Vol. 25 (2004), No. 1,
pp. 63-99.

Moberg, Christopher R. et al.: Do the management components of supply chain
management affect logistics performance? in: The International Journal of
Logistics Management, Vol. 15 (2004), No. 2, pp. 15-30.

Munson, Charles L., Jianli Hu and Meir J. Rosenblatt: Teaching the costs of
uncoordinated supply chains, in: Interfaces, Vol. 33 (2003), No. 3, May/June,
pp. 24-39.


n.a.: Worldwide Internet users will top 1 billion in 2005, Computer Industry
Almanac, Inc., press release September 3, 2004, http://www.c-ia.
com/pr0904.htm, retrieved on: February 15, 2006.

n.a.: 1978: The revolution begins, in: InfoWorld, Vol. 20, No. 43 1998, pp. 3-35.

n.a.: Energizing the supply chain: Trends and issues in supply chain management,
Deloitte Consulting, 1999.

n.a.: Creating unique customer experiences: The next stage of integrated product
development, Deloitte Research, 2000.

n.a.: Full Service, in: SAP INFO, No. 94, May 15, 2002.

n.a.: Why the Internet is still the 'next big thing', in: Supply Chain Management
Review (2002), May/June, pp. 54-58.

n.a.: The EPCglobal Network: Overview of design, benefits, and security,
EPCglobal, 2004.

n.a.: About the World Wide Web Consortium,
http://www.w3.org/Consortium/about-w3c.html, W3C, retrieved on July 17,
2006.

n.a.: The American Heritage Dictionary of the English Language,
http://www.bartleby.com/61/, Houghton Mifflin Company, retrieved on July 17,
2006.

n.a.: Cambridge Dictionaries Online, http://dictionary.cambridge.org/, Cambridge
University Press, retrieved on July 17, 2006.

n.a.: CERN's greatest achievements, http://www.cern.ch, CERN, retrieved on July
17, 2006.

n.a.: Computer history timeline, http://www.computerhistory.org/timeline/,
Computer History Museum, retrieved on July 17, 2006.

n.a.: Council of Supply Chain Management Professionals: http://www.cscmp.org,
2005, retrieved on: July 17, 2006.

n.a.: The EPCglobal Network: Enhancing the supply chain, VeriSign, Inc., White
Paper, 2005.

n.a.: Heinle's Newbury House Dictionary of American English,
http://nhd.heinle.com, Thomson Heinle, retrieved on July 17, 2006.

n.a.: International Organization of Standardization: http://www.iso.org, 2005,
retrieved on: July 17, 2006.

n.a.: Internet usage statistics - the big picture, 2005, Internet World Stats,
http://www.internetworldstats.com/stats.htm, retrieved on: July 17, 2006.


246 References

n.a.: Open Office.org, http://www.openoffice.org, Open Office.org, retrieved on
July 17, 2006.

n.a.: Report shows how Wal-Mart did it, in: RFID Journal, November 14, 2005,
http://www.rfidjournal.com, retrieved on November 18, 2005.

n.a.: Supply Chain Council, Inc.: http://www.supply-chain.org, 2005, retrieved on:
July 17, 2006.

n.a.: Supply chain operations reference model version 7.0, 2005, Supply-Chain
Council.

n.a.: U.S. Census Bureau, statistics of U.S. businesses,
http://www.census.gov/csd/susb/, U.S. Department of Commerce, retrieved on
July 17, 2006.

n.a.: World Wide Web Consortium, http://www.w3.org, W3C, retrieved on July
17, 2006.

n.a.: Total economy database, http://www.ggdc.net, The Conference Board and
Groningen Growth and Development Centre, retrieved on July 17, 2006.

Narasimhan, Ram and Jayanth Jayaram: Causal linkages in supply chain
management: An exploratory study of North American manufacturing firms, in:
Decision Sciences, Vol. 29 (1998), No. 3, pp. 579-605.

Narayanan, V. G. and Ananth Raman: Aligning incentives in supply chains, in:
Harvard Business Review, Vol. 82 (2004), November, pp. 94-102.

Naylor, Ben J., Mohamed M. Naim and Danny Berry: Leagility: Integrating the
lean and agile manufacturing paradigms in the total supply chain, in:
International Journal of Production Economics, Vol. 62 (1999), pp. 107-118.

Neuman, John and Samuels Christopher: Supply chain integration: vision or
reality? in: Supply Chain Management: An International Journal, Vol. 1 (1996),
No. 2, pp. 7-10.

New, Stephen J.: The scope of supply chain management research, in: Supply
Chain Management: An International Journal, Vol. 2 (1997), No. 1, pp. 15-22.

Nicolai, Sascha: eSupply Chain Management als strategisches
Managementkonzept, in: Wannenwetsch, Helmut H. and Sascha Nicolai (Eds.):
E-Supply-Chain-Management: Grundlagen, Strategien, Praxisanwendungen,
Wiesbaden 2002, pp. 1-11.

Nicolai, Sascha: Praxisinstrumente für eine erfolgreiche eSCM-Realisierung, in:
Wannenwetsch, Helmut H. and Sascha Nicolai (Eds.): E-Supply-Chain-
Management: Grundlagen, Strategien, Praxisanwendungen, Wiesbaden 2002,
pp. 69-99.


Nonaka, Ikujiro: A dynamic theory of organizational knowledge creation, in:
Organization Science, Vol. 5 (1994), No. 1, February, pp. 14-37.

Nunnally, Jum C.: Psychometric theory, 2nd ed., New York 1978.

Oliver, R. Keith and Michael D. Webber: Supply chain management: Logistics
catches up with strategy, in: Christopher, Martin (Ed.): Logistics: The strategic
issues, London 1992, pp. 63-75.

Park, Sung-Yeon and Gi Woong Yun: The impact of internet-based
communication systems on supply chain management: An application of
transaction cost analysis, in: Journal of Computer Mediated Communication,
Vol. 10 (2004), No. 1, November, p. Article 12.

Phan, Dien D.: E-business management strategies: A business-to-business case
study, in: Information Systems Management, Vol. 18 (2001), Fall, pp. 61-69.

Poirer, Charles C.: Achieving supply chain connectivity, in: Supply Chain
Management Review (2002), November/December, pp. 16-22.

Popper, Karl R.: The open society and its enemies - Band 2: The high tide of
prophecy: Hegel, Marx and the aftermath, 5th ed., London 1966, reprinted in
1973.

Porter, Michael E.: Competitive strategy, New York 1980.

Porter, Michael E.: Competitive advantage, New York 1985.

Porter, Michael E.: The competitive advantage of nations, New York 1990.

Porter, Michael E.: What is strategy? in: Harvard Business Review, Vol. 74
(1996), No. 6, pp. 61-79.

Porter, Michael E.: Strategy and the Internet, in: Harvard Business Review,
Vol. 79 (2001), March, pp. 63-78.

Porter, Michael E. and Victor E. Millar: How information gives you competitive
advantage, in: Harvard Business Review, Vol. 63 (1985), July/August, pp. 149


160.
Prahalad, C.K. and Gary Hamel: The core competence of the corporation, in:
Harvard Business Review, Vol. 68 (1990), May/June, pp. 79-91.

Prater, Edmund, Gregory V. Frazier and Pedro M. Reyes: Future impacts of RFID
on e-supply chains in grocery retailing, in: Supply Chain Management: An
International Journal, Vol. 10 (2005), No. 2, pp. 134-142.

Quiett, William Frank: Embracing supply chain management, in: Supply Chain
Management Review (2002), September/October, pp. 40-47.


248 References

Quinn, James B. and Frederick G. Hilmer: Strategic outsourcing, in: Sloan
Management Review, Vol. 35 (1994), No. 4, Summer, pp. 43-55.

Ranganathan, C., Jasbir S. Dhaliwal and Thompson S.H. Teo: Assimilation and
diffusion of web technologies in supply-chain management: An examination of
key drivers and performance impacts, in: International Journal of Electronic
Commerce, Vol. 9 (2004), No. 1, pp. 127-161.

Repenning, Nelson P. and John D. Sterman: Nobody ever gets credit for fixing
problems that never happened, in: California Management Review, Vol. 43
(2001), No. 4, Summer, pp. 64-88.

Ringle, Christian M.: Messung von Kausalmodellen, 2004, Industrielles
Management Working Papers (No. 14), Hamburg.

Robbins, Stephen P.: Organizational behavior, 11th ed., Upper Saddle River 2005.

Robinson Jr., E. Powell, Funda Sahin and Li-Lian Gao: The impact of e-
replenishment strategy on make-to-order supply chain performance, in:
Decision Sciences, Vol. 36 (2005), No. 2, pp. 33-64.

Rossiter, John: The C-OAR-SE procedure for scale development in marketing, in:
International Journal of Research in Marketing, Vol. 19 (2002), pp. 305-335.

Rousseau, Denise M. et al.: Not so different after all: A cross-discipline view of
trust, in: The Academy of Management Review, Vol. 23 (1998), No. 3, July, pp.
393-404.

Russell, Roberta S. and Bernard W. Taylor III: Operations management, 3rd ed.,
Upper Saddle River 2000.

Sabherwal, Rajiv and Sanjiv Sabherwal: Knowledge management using
information technology: Determinants of short-term impact on firm value, in:
Decision Sciences, Vol. 36 (2005), No. 4, December, pp. 531-567.

Sahin, Funda and E. Powell Robinson Jr.: Flow coordination and information
sharing in supply chains: Review, implications, and directions for future
research, in: Decision Sciences, Vol. 33 (2002), No. 4, Fall, pp. 505-536.

Samaranayake, Premaratne: A conceptual framework for supply chain
management: a structural integration, in: Supply Chain Management: An
International Journal, Vol. 10 (2005), No. 1, pp. 47-59.

Samuelson, Paul: Foundations of economic analysis, Cambridge, MA 1947.

Sanders, Nada R. and Robert Premus: IT applications in supply chain
organizations: A link between competitive priorities and organizational
benefits, in: Journal of Business Logistics, Vol. 23 (2002), No. 1, pp. 65-83.


Sanders, Nada R. and Robert Premus: Modeling the relationship between firm IT
capability, collaboration, and performance, in: Journal of Business Logistics,
Vol. 26 (2005), No. 1, pp. 1-23.

Scheffler, Wolfram et al.: Entwicklungsperspektiven im Electronic Business,
Wiesbaden 2000.

Schmenner, Roger W. and Morgan L. Swink: On theory in operations
management, in: Journal of Operations Management, Vol. 17 (1998), pp. 97


113.
Schonberger, Richard J.: World class manufacturing - the lessons of simplicity
applied, New York London 1986.

Schönsleben, Paul: Integrales Logistikmanagement: Planung und Steuerung der
umfassenden Supply Chain, 4th ed., Berlin Heidelberg New York 2004.

Schreiner, Wilhelm: Entwicklungen und Implementierung von SCM-Strategien,
in: Wannenwetsch, Helmut H. (Ed.): Vernetztes Supply Chain Management,
Berlin Heidelberg New York 2005, pp. 381-396.

Schroeder, Roger G. and Barbara B. Flynn: High performance manufacturing global
perspectives, New York 2001.

Schroeder, Roger G. and Barbara B. Flynn: High performance manufacturing: Just
another fad? in: Schroeder, Roger G. and Barbara B. Flynn (Eds.): High
performance manufacturing - global perspectives, New York 2001, pp. 3-17.

Schubert, Petra, Dorian Selz and Patrick Haertsch: Digital erfolgreich, Fallstudien
zu strategischen E-Business-Konzepten, Berlin Heidelberg 2001.

Sellin, Norbert: Partial least squares modeling in research on educational
achievement, in: Bos, Wilfried and Rainer H. Lehmann (Eds.): Reflections on
educational achievement, Münster New York München Berlin 1995, pp. 256


267.
Senge, Peter M.: The fifth discipline, New York 1990.

Sharma, Nikhil: The origin of the DIKW hierarchy, 2005, http://wwwpersonal.
si.umich.edu/~nsharma/dikw_origin.htm, retrieved on: July 17, 2006.

Sharman, Graham: How the internet is accelerating supply chain trends, in:
Supply Chain Management Review (2002), March/April, pp. 18-26.

Shaw, Michael J.: Information-based manufacturing with the Web, in: The
International Journal of Flexible Manufacturing Systems, Vol. 12 (2000),
No. 2,3, April, pp. 115-129.

Sheffi, Yossi: RFID and the innovation cycle, in: The International Journal of
Logistics Management, Vol. 15 (2004), No. 1, pp. 1-10.


250 References

Siau, Keng and Yuhong Tian: Supply chains integration: architecture and enabling
technologies, in: The Journal of Computer Information Systems, Vol. 44
(2004), No. 3, Spring, pp. 67-72.

Simchi-Levi, David, Philip Kaminsky and Edith Simchi-Levi: Designing and
managing the supply chain: Concepts, strategies, and case studies, 2nd ed., New
York 2003.

Skinner, Wickham: Manufacturing - missing link in corporate strategy, in:
Harvard Business Review, Vol. 47 (1969), May/June, pp. 136-145.

Slack, Nigel, Stuart Chambers and Robert Johnston: Operations management, 4th
ed., Harlow 2004.

Sommerlatte, Tom and Michael Mollenhauer: Qualität, Kosten, Zeit - Das
magische Dreieck, in: Little, Arthur D. (Ed.): Management von Spitzenqualität,
Wiesbaden 1992, pp. 26-36.

Spekman, Robert E., John W. Kamauff Jr. and Niklas Myhr: An empirical
investigation into supply chain management: A perspective on partnerships, in:
Supply Chain Management: An International Journal, Vol. 3 (1998), No. 2, pp.
53-67.

Spengler, Joseph J.: Vertical integration and antitrust policy, in: Journal of
Political Economy (1950), pp. 347-352.

Stadtler, Hartmut: Supply chain management - an overview, in: Stadtler, Hartmut
and Christoph Kilger (Eds.): Supply chain management and advanced planning:
concepts, models, software and case studies (2nd ed.), Berlin Heidelberg New
York 2002, pp. 7-28.

Stalk, George and Thomas M. Hout: Competing against time, London 1990.

Stalk, George, Philip Evans and Lawrence E. Shulman: Competing on
capabilities: The new rules of corporate strategy, in: Harvard Business Review,
Vol. 70 (1992), No. 2, March/April, pp. 57-69.

Stank, Theodore P., Patricia J. Daugherty and Alexander E. Ellinger:
Marketing/logistics integration and firm performance, in: The International
Journal of Logistics Management, Vol. 10 (1999), No. 1, pp. 11-24.

Stank, Theodore P., Scott B. Keller and Patricia J. Daugherty: Supply chain
collaboration and logistical service performance, in: Journal of Business
Logistics, Vol. 22 (2001), No. 1, pp. 29-48.

Sterman, John D.: Modeling managerial behavior: Misperceptions of feedback in a
dynamic decision making experiment, in: Management Science, Vol. 35 (1989),
No. 3, pp. 321-339.


Sterman, John D.: Business Dynamics, Boston 2000.

Stevens, Graham C.: Integrating the supply chain, in: International Journal of
Physical Distribution & Materials Management, Vol. 19 (1989), No. 8, pp. 3-8.

Subramani, Mani: How do suppliers benefit from information technology use in
supply chain relationships? in: MIS Quarterly, Vol. 28 (2004), No. 1, pp. 45-73.

Suzaki, Kiyoshi: The new manufacturing challenge, New York 1987.

Swaminathan, Jayashankar M.: Enabling customization using standardized
operations, in: California Management Review, Vol. 43 (2001), No. 3, Spring,
pp. 125-135.

Swaminathan, Jayashankar M. and Sridhar R. Tayur: Models for supply chains in
e-business, in: Management Science, Vol. 49 (2003), No. 10, pp. 1387-1406.

Tan, Gek Woo, Michael J. Shaw and William Fulkerson: Web-based supply chain
management, in: Information Systems Frontier, Vol. 2 (2000), No. 1, January,
pp. 41-55.

Tan, Keah Choon: A framework of supply chain management literature, in:
European Journal of Purchasing & Supply Management, Vol. 7 (2001), No. 1,
pp. 39-48.

Tarn, Michael J., David C. Yen and Marcus Beaumont: Exploring the rationales
for ERP and SCM integration, in: Industrial Management & Data Systems, Vol.
102 (2002), No. 1, pp. 26-35.

Thonemann, Ulrich et al.: Supply chain excellence im Handel, Wiesbaden 2005.

Thonemann, Ulrich et al.: Supply chain champions, Wiesbaden 2003.

Towill, Denis R.: The seamless supply chain - the predator's strategic advantage,
in: International Journal of Technology Management, Vol. 17 (1997), No. 1, pp.
37-56.

Trent, Robert J.: Applying TQM to SCM, in: Supply Chain Management Review
(2001), May/June, pp. 70-78.

Vakharia, Asoo J.: E-business and supply chain management, in: Decision
Sciences, Vol. 33 (2002), No. 4, Fall, pp. 495-504.

von Bertalanffy, Ludwig: General system theory: Foundations, development,
applications, New York 1968.

Walker, William T.: Unbundling the corporation: A blueprint for supply chain
networks, in: McCormack, Kevin and William C. Johnson (Eds.): Supply chain
networks and business process orientation, Boca Raton, FL 2003.


252 References

Walther, Johannes: Konzeptionelle Grundlagen des Supply Chain Managements,
in: Walther, Johannes (Ed.): Supply Chain Management, Frankfurt am Main
2001, pp. 11-31.

Wathen, Samuel: Manufacturing strategy in business units, in: International
Journal of Operations & Production Management, Vol. 15 (1994), No. 8, pp. 4


13.
Watson, Richard T. et al.: Electronic commerce: The strategic perspective, Fort
Worth 2000.

Weber, Jürgen, Andreas Bacher and Marcus Groll: Supply Chain Controlling, in:
Busch, Axel and Wilhelm Dangelmaier (Eds.): Integriertes Supply Chain
Management: Theorie und Praxis effektiver unternehmensübergreifender
Geschäftsprozesse (2nd ed.), Wiesbaden 2004, pp. 147-167.

Weidemann, Tobias: Open Office 2.0: Das beste Office der Welt, in: PC-Welt,
No. 12/2005, December, 2005, pp. 48-52.

Werner, Hartmut: Supply Chain Management: Grundlagen, Strategien,
Instrumente und Controlling, 2nd ed., Wiesbaden 2002.

Wernerfelt, Birger: A resource-based view of the firm, in: Strategic Management
Journal, Vol. 5 (1984), No. 2, pp. 171-180.

Werts, Charles E., Robert L. Linn and Karl G. Jöreskog: Interclass reliability
estimates: Testing structural assumptions, in: Educational and Psychological
Measurement, Vol. 34 (1974), pp. 25-33.

Williamson, Oliver E.: Markets and hierarchies: Analysis and antitrust
implications, New York 1975.

Williamson, Oliver E.: The economic institutions of capitalism: Firms, markets,
relational contracting, New York 1985.

Williamson, Oliver E.: Economic organizations: Firms, markets and policy
control, Bodmin 1986.

Williamson, Oliver E.: Comparative economic organization: The analysis of
discrete structural alternatives, in: Administrative Science Quarterly, Vol. 36
(1991), No. 2, pp. 269-296.

Wisner, Joel D.: A structural equation model for supply chain management
strategies and firm performance, in: Journal of Business Logistics, Vol. 24
(2003), No. 1, pp. 1-26.

Wöhe, Günter and Ulrich Döring: Einführung in die allgemeine
Betriebswirtschaftslehre, 22nd ed., München 2005.


Wold, Herman: Path models with latent variables: The NIPALS approach, in:
Blalock, Hubert M. (Ed.): Quantitative sociology: International perspectives on
mathematical and statistical modeling, New York 1975, pp. 307-357.

Wold, Herman: Soft modeling: The basic design and some extensions, in:
Jöreskog, Karl G. and Herman Wold (Eds.): Systems under indirect
observation, Part II, Amsterdam 1982, pp. 1-54.

Zeleny, M.: Management support systems: Towards integrated knowledge
management, in: Human Systems Management, Vol. 7 (1987), No. 1, pp. 59-70.

Zsidisin, George A., Gary L. Ragatz and Steven A. Melnyk: The dark side of
supply chain management, in: Supply Chain Management Review (2005),
March, pp. 46-52.


Appendices

Appendix 1

Normative Level
-Customer orientation
-Cooperative orientation
-Systemic, holistic view of supply chains
-Process orientation
-Contingency approach
Strategic Level
Strategic physical
and technical tools /
infrastructure
Prerequisite strategic
manage ment decisions
Core SCM model:
SCM Cooperation
cooperative, long-term relationships,
based on normative SCM attributes
Coordination
-communication
-information exchange
-information sharing
-process visibility
-strategic alignment
Collaboration
-interaction
-knowledge sharing
-involvement
-joint planning and
control
-continuous
improvement
-cross-functional,
cross-company teams
Integration
-seamless material
and information flows
E-Business
-ERP / IT systems
-Advanced planning
systems
-Software support
-Internet technology
Location and Facilities
Competitive priorities (cost,
quality, time, and flexibility)
Supply chain structure (roles,
functions, and activities of
supply chain members)
SCM processes (ECR, CPFR,
CRM, manufacturing flows,
logistics, procurement…)
Trust Common SC understanding Acknowledgment of interdependencies
Commitment Risk and reward sharing Willingness of SC alignment (vision, processes, goals)
Top management support Accountability/ responsibility
SCM antecedents (strategic attitudes)
Operational Level
Physical & technical management components
Planning and control methods (optimization, standardizat ion)
Work flow / activity structure
Organization structure
Co mmunication and in formation flo w facility structure
Product flow facility structure
Management components should reflect strategic and normative direction.
Managerial & behavioral management components
Management methods (e.g. continuous improvement, management by fact)
Power and leadership structure
Risk and reward structure, incl. supply chain measurement system
Culture and attitude

Figure Appendix-1: Details of third generation SCM framework


256 Appendices

Appendix 2

Table Appendix-1: Overview of applications seen to be especially relevant for SCM

Master production schedule
Material requirements planning
Capacity requirements planning
Finite capacity scheduling
Shop floor control
Inventory management
Purchasing
Demand planning
Order management
Distribution management
Product data management
Quality documentation management
Quality control and improvement
Performance measurement system
Workflow management
Business intelligence
Simulation and optimization of
production and logistics planning
Groupware tools
Product configuration


Appendix 3

Table Appendix-2: Scale “internal coordination”

 Factor Loading
Our corporation implements ordering and stock management policies,
on a global scale, in order to coordinate distribution. 0.795
Our corporation performs aggregate planning for plants, according to
our global distribution needs. 0.765
Managerial innovations are transferred among plants within our
corporation. 0.795
Our corporation transfers technological innovations and know-how
between plants. 0.756
Sales and manufacturing personnel communicate well with each other
in this organization. 0.651
Cronbach’s alpha:
Variance explained:
0.806
56.90%

Table Appendix-3: Scale “external coordination”

 Factor Loading
We actively plan supply chain activities. 0.799
We consider our customers’ forecasts in our supply chain planning. 0.644
We strive to manage each of our supply chains as a whole. 0.747
We monitor the performance of members of our supply chains, in
order to adjust supply chain plans. 0.762
We gather indicators of supply chain performance. 0.871
Cronbach’s alpha:
Variance explained:
0.810
57.17%


258 Appendices

Table Appendix-4: Scale “internal collaboration”

 Factor Loading
Our plant forms teams to solve problems. 0.849
In the past three years, many problems have been solved through
small group sessions. 0.878
Problem solving teams have helped improve manufacturing processes
at this plant. 0.850
Employee teams are encouraged to try to solve their own problems,
as much as possible. 0.753
We don’t use problem solving teams much, in this plant. (Reverse.) 0.853
Cronbach’s alpha:
Variance explained:
0.893
70.18%

Table Appendix-5: Scale “external collaboration”

 Factor Loading
We work as a partner with our customers. 0.460
We maintain cooperative relationships with our suppliers. 0.850
We provide a fair return to our suppliers. 0.625
We help our suppliers to improve their quality. 0.840
We maintain close communications with suppliers about quality
considerations and design changes. 0.879
Cronbach’s alpha:
Variance explained:
0.784
56.01%


Table Appendix-6: Scale “customer orientation”

 Factor Loading
We frequently are in close contact with our customers. 0.849
Our customers give us feedback on our quality and delivery
performance. 0.863
Our customers are actively involved in our product design process. 0.689
We strive to be highly responsive to our customers’ needs. 0.716
Cronbach’s alpha:
Variance explained:
0.779
61.33%

Table Appendix-7: Scale “trust”

 Factor Loading
We are comfortable sharing problems with our suppliers. 0.843
In dealing with our suppliers, we are willing to change assumptions,
in order to find more effective solutions. 0.613
We believe that cooperating with our suppliers is beneficial. 0.825
We emphasize openness of communications in collaborating with our
suppliers. 0.823
Cronbach’s alpha:
Variance explained:
0.771
61.06%

Table Appendix-8: Scale “customer satisfaction”

 Factor Loading
Our customers are pleased with the products and services we provide
them. 0.881
Our customers seem happy with our responsiveness to their problems. 0.778
Customer standards are always met by our plant. 0.811
Our customers have been well satisfied with the quality of our
products, over the past three years. 0.871
In general, our plant's level of quality performance over the past three
years has been low, relative to industry norms. (Reverse.) 0.702
Cronbach’s alpha:
Variance explained
0.863
65.84%


260 Appendices

Table Appendix-9: Scale “distinctive competencies”

 Factor Loading
Competitive position globally in supplier relation. 0.805
Competitive position globally in customer relation. 0.694
Competitive position globally in enterprise resource planning. 0.689
Competitive position globally in quality improvement program. 0.814
Competitive position globally in SCM. 0.758
Competitive position globally in JIT. 0.680
Cronbach’s alpha:
Variance explained
0.830
55.06%


Short Curriculum Vitae Andreas Hammer

University Education

11/1996 – 09/1998
Undergraduate studies in business administration at the
University of Nürnberg-Erlangen. Degree: Vordiplom

10/1998 – 03/2002
Graduate studies in business administration at the University
of Mannheim. Degree: Diplom-Kaufmann

08/1999 – 08/2000
Graduate studies at Creighton University, USA.
Degree: Master of Business Administration

09/2002 – 05/2006
Postgraduate studies at the University of Mannheim,
Department of Operations Management Prof. Dr. Milling.
Degree: Dr. rer. pol.

Professional Assignments

11/1995 – 07/1999 Fa. Jörg Hammer GmbH, Pforzheim. Project work.
10/2000 – 04/2002 UDF Consulting AG, Stuttgart. Junior Consultant.
05/2002 – 05/2006 Andreas Hammer Consulting, Pforzheim. Consultant.
09/2002 – 02/2006 International University in Germany, Bruchsal,

Department of Operations Management Prof. Dr. Maier.
Research and Teaching Associate.
08/2004 – 04/2006 nordic, Stuttgart. Design fashion and lifestyle products.
Managing Director.

Since 06/2006 Bain & Company, Munich. Consultant.


The term Supply Chain management (SCM) has become a cornerstone term in the academic business literature as well as in general publications on business management. Over the last two decades, many different viewpoints, concepts and frameworks have been introduced to the literature. Yet, a comprehensive structure that puts the underlying ideas in an overall context is still missing. Furthermore, empirical evidence of causes and effects in SCM is limited. An extensive meta-analysis has been conducted to provide one comprehensive, sound, and consistent SCM framework. This „third generation“ SCM framework distinguishes between a normative, a strategic, and an operative level. Its core is situated on the strategic level and builds around the three carefully separated elements coordination, collaboration, and integration. The analysis shows that these terms have to be distinguished and that they are all important levers for successful SCM. The international research project High Performance
Manufacturing (HPM) builds the basis for the empirical part of this text. A descriptive
analysis shows that so-called SCM champions achieve higher performance levels than so-called SCM laggards. Then, the third generation SCM framework is validated empirically through structural equation modeling, i.e. by means of Partial Least Squares (PLS). The objective has been to not only show the beneficial impact of SCM on plant performance, but also to identify and better understand the exact drivers of this performance and their individual impact. As such key success drivers are identified: internal collaboration, customer
orientation, internal coordination, ERP adoption (as a proxy for integration), external collaboration, and trust. In addition to this core content, the book also describes key challenges for SCM ideas to resolve them. The impact identified in this text and the embracing SCM show that SCM is not a fad but - if understood and applied correctly management framework with sustaining impact on organizational

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