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Warns-9.1

STRATEGIC EVALUATION OF THE OPTIMAL ACQUISITION
OF NEW PROCESS TECHNOLOGY

BY

Hyun Gyu Kim

A DISSERTATION

Submitted to
Michigan State University
in partial fialfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Management

1993

 

 

ABSTRACT

STRATEGIC EVALUATION OF THE OPTHVIAL ACQUISITION
OF NEW PROCESS TECHNOLOGY

By

Hyun Gyu Kim

This dissertation focuses on the strategic decisions involved in optimal investment
policies for the acquisition of new process technology. Here, optimal investment policies
include not only the acquisition of new process technology but also the attrition of existing
manual production capacity. This study explores analytically why and how the technology
decisions involving these optimal investment policies are related to corporate strategy and
external market environment. This dissertation also presents a conceptual framework for
investment decisions regarding the acquisition of new process technology. This
framework explains the relationships among decision variables and other variables used in
the development of a model.

The model developed here is a dynamic optimal control model. It has an objective
function which maximizes the long-term value of a firm's strategic business unit (SBU) and
also minimizes both tangible and intangible costs associated with the use and acquisition of
process technology during the planning horizon. These cost factors include not only the
direct costs associated with the acquisition and attrition of process technology but also the
cost associated with the flexibility attribute of process technology. It is assumed here that
there are two components to process technology, capacity (automation capacity) and
flexibility. Capacity refers to the total volume that can be produced by the system,
whereas flexibility refers to the variety of product (part) types that the system can handle.
Other cost factors in the objective function are related to the penalty costs associated with
deviations between an SBU’s actual market demand and its goals, as well as deviations

between its actual market demand and its production level. These deviations are measured

 

 

 

 

 

in terms of both volume and variety. The constraints address the change in market

potential for volume and variety, the change in unit production cost due to the acquisition
of new process technology and flexibility, and the change in system capacity and flexibility.
The optimal solution of the model provides decisions regarding both the acquisition of
new process technology affecting the system capacity and flexibility, and the attrition of
the conventional (existing) production capacity.

A scenario analysis is conducted to examine several research issues. The results of
scenario analysis lead to some insightful conclusions. First, in the long run for the optimal
performance of a firm's manufacturing systems it should select a strategic priority which is
congruent to external market conditions. Second, the acquisition policy of new process

technology is affected by the type of product life cycle (external market conditions).

 

 

 

Copynght
by
IIyun(3yul§hn

1993

 

Dedicated
to

my father

 

ACKNOWLEDGMENTS

My deepest gratitude goes to dissertation advisor, Dr. Soumen Ghosh, for his
support and guidance throughout this dissertation research and the Ph.D. program. His
kindness allowed me to finish this dissertation. I also wish to express my sincere
appreciation to the members of the dissertation committee, Dr. Michael Moch, Dr. Paul
Rubin, and Dr. Shawnee Vickery, for their valuable comments and suggestions. In
addition, I want to acknowledge my indebtedness to Dean Gill Lim of International
Studies and Program who always supported and encouraged me.

I wish to cite the contribution of my mother, who has supported my education for
more than thirty years. I also thank my wife and children. Without their patience and
sacrifice, it would have been difficult to finish this dissertation. Finally, I wish to thank all
the teachers and friends of my life whose encouragement has been always a prime

motivation for my study.

vi

 

TABLE OF CONTENTS

page
LIST OF TABLES . . . . . . . . . xiv
LIST OF FIGURES . . . . . . . . . xvii
CHAPTER
I. INTRODUCTION . . . . . . . . 1
1.1 OBJECTIVES OF RESEARCH 2
1.1 RESEARCH ISSUES . . . . . . 5
1.1.1 Flexibility and Cost Efficiency of
New Process Technology . . . . 5
1.1.2 Acquisition of New Process Technology
and Evolution of Technology Choice . . . 8
1.2 RESEARCH QUESTIONS . . . . . 10
1.2.1 Questions for The Acquisition of
New Process Technology . . . . 10
1.2.2 Questions for The Evolution of
Technology Choice . . . . . l 1
1.4 SUMMARY . . . . . . . . 11
II. LITERATURE REVIEW . . . . . . . 13
2.1 MANUFACTURING FLEXIBILITY . . . . 14

2.1.1 Definition of Flexibility . . . . 15

vii

 

2.2

 

Manufacturing Flexibility and
Its Strategic Implications

2.1.2.1 Definition of Product Mix Flexibility
and Volume Flexibility

2.1.2.2 Strategic Value of Manufacturing Flexibility.
Measurement of Manufacturing Flexibility
Trade-off Between Flexibility and Efficiency .

Summary and Implications

PROCESS TECHNOLOGY AND

2.2.1

2.2.2

PRODUCT LIFE CYCLE

Process Technology in Manufacturing Strategy
2.2.1.1 Characteristics of Process Technology
2.2.1.2 Process Technology in Capacity Planning

2.2.1.3 Process Technology in
Manufacturing Strategy

Product Life Cycle in Manufacturing Strategy

2.2.2.1 Product Volume and Product Mix in
the Product Life Cycle

2.2.2.2 Manufacturing Cost in PLC

2.2.2.3 Reversal of PLC

Integration of Product and Process Decisions
2.2.3.1 Product-Process Matrix

2.2.3.2 Effect of Flexible Technology
on Product-Process Matrix

2.2.4 Summary and Implications

viii

17

17

18

20

22

24

25

25

27

29

2.3 NEW PROCESS TECHNOLOGY AND

MANUFACTURING FLEXIBILITY . . . 39
2.3.1 New Process Technology . . . . . 39
2.3.1.1 Definition of New Process Technology . . 39
2.3.1.2 Rigid Automation and Flexible Automation . 39

2.3.1.3 Measurement for the Flexibility

of New Process Technology . . . 41

2.3.2 Effects of New Process Technology . . . . 42
2.3.2.1 Effects on Manufacturing Lead Time . . 43
2.3.2.2 Effects on Cost Structure . . . . 43
2.3.2.3 Effects on Sales Growth . . . . 44

2.3.3 New Process Technology and Learning Effect . . 45
2.3.4 Analytic Studies for New Process Technology . . 47
2.3.4.1 The Model of Fine and Freund . . . 47
2.3.4.2 The Model of Gaimon . . . . 49

2.3.4.3 The Model ofRoth et al. . . . . 50

2.3.5 Summary and Implications . . . . 52
2.4 RESEARCH STATEMENT . . . . . 52

III. STRUCTURAL FRANIEWORK FOR THE STRATEGIC
ACQUISITION OF NEW PROCESS TECHNOLOGY:

A CONCEPTUAL FRAMEWORK . . . . 54
3.1 DECISION PROCESS OF NEW
TECHNOLOGY ACQUISITION . . . . 55
3.2 TECHNOLOGY AND EXTERNAL ENVIRONMENT . . 59
3 .3 TECHNOLOGY AND

MANUFACTURING COST STRUCTURE . . . 6O

ix

 

3 .4 PRODUCT-TECHNOLOGY BOX
THE MODEL
4. 1 INTRODUCTION
4.1.1 General Description of The Model
4.1.2 Assumptions .
4.1.3 Variables
4.1.3.1 Endogenous Variables
4.1.3.2 Exogenous Variables
4.2 THE OBIECTIVE FUNCTION
4.3 CONSTRAINTS
4.3.1 Change in Market Demand
4.3.2 Change in Variable Costs

4.3.3 Change in Production Capacity

4.3.4 Change in Cumulative Capacity of New Technology.

4.3.5 Change in Overall System Flexibility
4.3.6 Inequality Constraints .
4.3.7 Variable Boundaries
4.4 SUMIVIARY
SOLUTION APPROACH
5.1 OPTIMAL POLICIES
5.1.1 Necessary Conditions

5.1.2 Optimal Policies for Acquiring New Technology

62

68

68

68

69

7O

7O

71

76

76

78

79

80

81

82

82

84

84

84

86

 

5.2

5.1.3 Optimal Policies for Reducing Conventional Capacity
5.1.4 Optimal Policies for Production Rates

SOLUTION ALGORITHM

5.2.1 Discretized Non-Linear Version of the Model.

5.2.2 Limitations

VI. SCENARIO ANALYSIS

6.1

6.2

OVERVIEW OF DESIGN FOR SCENARIO ANALYSIS .
6.1.1 Strategic Choices and the PLC
6.1.2 Positioning of Initial Process Technology
6.1.3 Settings of Variables .
6.1.4 Values of Exogenous Variables
6.1.5 Hypothetical PLC curves
6.1.6 Frameworks of Design
6.1.7 Performance Measures
RESULTS OF ANALYSIS
6.2.1 Analysis with the Normal PLC
6.2.1.1 Effect of Initial Process Technology .
6.2.1.2 Effect of Strategic Choice
6.2.1.3 Conclusion
6.2.2 Analysis with the Reversed PLC
6.2.2.1 Effect of Initial Process Technology .
6.2.2.2 Effect of Strategic Choice
6.2.2.3 Conclusion

xi

89

9O

92

92

93

95

95

95

97

99

114

114

114

116

118

119

119

120

122

 

6.2.3 Comparison Between the Normal PLC
and the Reversed PLC.

6.2.3.1 Optimal Acquisition of
New Process Technology

6.2.3.2 Evolution of Process Technology
6.2.3.3 Cost Performance
6.3 SUMMARY OF FINDINGS .
6.3.1 Acquisition of New Process Technology
6.3.2 Evolution of Process Technology
6.3.3 Best Strategic Choice
6.3.4 Conclusion
VII. CONCLUSIONS
7.1 RESEARCH OVERVIEW
7.1.1 Research Scope and Issues
7.1.2 Research Model
7.1.3 Research Methodology
7.1.4 Summary of Findings .
7.1.5 Limitations of Study
7.2 CONTRIBUTIONS
7.3 POSSIBLE RESEARCH EXTENSIONS
APPENDICES
I. Derivation of Necessary Conditions

11. An Example of Model in Spreadsheet.

xii

124

125

125

126

126

173

174

174

174

175

176

177

179

180

 

 

 

 

BIBLIOGRAPHY . . . . . . . . . 183

xiii

 

Table

2.1

3.1

6.1

6.2

6.3

6.4

6.5

6.6

6.7

6.8

6.9

6.10

6.11

6.12

6.13

LIST OF TABLES

Characteristics of Process Choice

Objectives of the Acquisition of New Technology
Strategic Choices and the Normal PLC

Strategic Choices and the Reversed PLC
Variable Settings for the Stages of the PLC
Variable Settings for Strategic Priority

Value Settings of Research Variables
Related to the Strategic Choice

Variable Settings for the Positioning of
Initial Technology Choice

Value Settings for the Research Variables
Related to Initial Process Technology.

Value Settings for Exogenous Variables
Values of Variables of the Normal PLC
Values of Variables of the Reversed PLC
Design Framework with the Normal PLC
Design Framework with the Reversed PLC

Settings of Research Variables

xiv

page

26

58

96

97

99

100

101

102

102

104

106

107

110

110

111

 

 

6.14

6.15

6.16

6.17

6.18

6.19

6.20

6.21

6.22

6.23

6.24

6.25

6.26

6.27

6.28

6.29

6.30

6.31

6.32

6.33

Value Settings of Research Variables .

Acquisition of Automation Capacity with the Normal PLC
Acquisition of Flexibility with the Normal PLC
Cumulative Automation Capacity with the Normal PLC
Cumulative System's Flexibility with the Normal PLC
Acquisition of Automation Capacity with the Normal PLC
Acquisition of Flexibility with the Normal PLC
Cumulative Automation Capacity with the Normal PLC

Cumulative System's Flexibility with the Normal PLC

Acquisition of Automation Capacity with the Reversed PLC.

Acquisition of Flexibility with the Reversed PLC
Cumulative Automation Capacity with the Reversed PLC
Cumulative System's Flexibility with the Reversed PLC
Acquisition of Automation with the Reversed PLC
Acquisition of Flexibility with the Reversed PLC
Cumulative Automation Capacity with the Reversed PLC
Cumulative Flexibility with the Reversed PLC

Cost Performance

Acquisition of Automation Capacity with the Normal PLC
(el = 10, e2 = 20)

Acquisition of Flexibility with the Normal PLC
(el = 10, e2 = 20)

Cumulative Automation Capacity with the Normal PLC
(el = 10, e2 = 20)

112

129

130

134

137

141

142

145

146

157

158

161

164

166

 

 

6.35 Cumulative System's Flexibility with the Normal PLC
(el = 10, e2 = 20) . . . . . . . 167

 

xvi

 

Figure
2.1
2.2
2.3
2.4

2.5

b.)
p—J

3.2

3.3

3.4

3.5

3.6

3.7

6.1

6.2

6.3

6.4

LIST OF FIGURES

Variety-Related Costs and Scale-Related Costs

Product and Process Matrix

The Effect of Flexible Automation on the Product-Process Matrix
Integration of Automation and PLC

Four Stages of Possible Evolution of the PLC

Structural Diagram of the Decision Process for
the Acquisition of New Advanced Technology

Linkage Among Various Decisions

Technology and Investment Cost

Automation and Average Volume-Related Variable Costs
Flexibility and Average Variety-Related Variable Costs
Technology Matrix

Product-Technology Box

Four Types of Initial Technology Choice

Positioning of Initial Technology Choice

Changes in Total Market Volume of the Normal PLC

Changes in Total Market Variety of the Normal PLC

xvii

64

66

98

98

108

108

 

 

6.5

6.6

6.7

6.8

6.9

6.10

6.11

6.12

6.13

6.14

6.15

6.16

6.17

6.18

6.19

6.20

6.21

6.22

6.23

6.24

6.25

6.26

Changes in Total Market Volume of the Reversed PLC
Changes in Total Market Variety of the Reversed PLC
Acquisition of Automation with the Normal PLC
Acquisition of Flexibility with the Normal PLC
Cumulative Automation Capacity with the Normal PLC
Cumulative System's Flexibility with the Normal PLC
Acquisition of Automation Capacity with the Normal PLC
Acquisition of Flexibility with the Normal PLC
Cumulative Automation Capacity with "the Normal PLC
Cumulative Flexibility with the Normal PLC

Acquisition of Automation with the Reversed PLC
Acquisition of Flexibility with the Reversed PLC
Cumulative Automation Capacity with the Reversed PLC

Cumulative system's Flexibility with the Reversed PLC

Acquisition of Automation Capacity with the Reversed PLC.

Acquisition of Flexibility with the Reversed PLC
Cumulative Automation Capacity with the Reversed PLC
Cumulative System's Flexibility with the Reversed PLC
Evolution of Process Technology with the Normal PLC
Evolution of Process Technology with the Reversed PLC

Acquisition of Automation Capacity with the Normal PLC
(el = 10. e2 = 20)

Acquisition of Flexibility with the Normal PLC
(e1=10,e2=20) .

xviii

109

109

131

132

135

136

139

140

144

147

148

151

152

155

156

159

160

168

169

 

 

6.27 Cumulative Automation Capacity with the Normal PLC
(e1= 10, e2=20) . . . . . . . 170

6.28 Cumulative System's Flexibility with the Normal PLC
(el = 10, e2=20) . . . . . . . 171

6.29 Evolution of Process Technology with the Normal PLC
(el = 10, e2=20) . . . . . . . 172

xix

 

CHAPTER I

INTRODUCTION

This dissertation focuses on the strategic decisions involved in optimal investment
policies for the acquisition of new process technology. Here, optimal investment policies
include not only the acquisition of new process technology1 but also the attrition of
existing manual production capacity. This study explores analytically why and how the
technology decisions involving these optimal investment policies are related to corporate
strategy and external market environment. This dissertation also presents a conceptual
framework for investment decisions regarding the acquisition of new process technology.
This framework explains the relationships among decision variables and other variables2
used in the development of a model.

The model develdped here is a dynamic optimal control model. It has an objective
firnction which maximizes the long-term value of a firm's strategic business unit (SBU) and
also minimizes both tangible and intangible costs associated with the use and acquisition of
process technology during the planning horizon. These cost factors include not only the
direct costs associated with the acquisition and attrition of process technology but also the
cost associated with the flexibility attribute of process technology. It is assumed here that
there are two components to process technolog , capacity (automation capacity) and
flexibility. Capacity refers to the total volume that can be produced by the system,
whereas flexibility refers to the variety of product (part) types that the system can handle.

Other cost factors in the objective firnction are related to the penalty costs associated with

 

1 Hereafter, "new process technology" is sometimes referred to as "new technology."
2 In an optimal control model, decision variables are called "control variables." and the other
variables are called "state variables."

 

 

2

deviations between an SBU's actual market demand and its goals, as well as deviations
between its actual market demand and its production level. These deviations are measured
in terms of both volume and variety. The constraints address the change in market
potential for volume and variety, the change in unit production cost due to the acquisition
of new process technology and flexibility, and the change in system capacity and flexibility.
The optimal solution of the model provides decisions regarding both the acquisition of
new process technology affecting the system capacity and flexibility, and the attrition of
the conventional (existing) production capacity.

From the scenario analysis using the model, this study attempts to evaluate two
strategic issues found frequently in the literature. One is the effect of the strategic
emphasis on flexibility over cost efficiency on optimal policies, and the other is the effect
on the changes in the position of technology choice on the product - technology matrix3
due to technology acquisition. These two issues are evaluated within the framework of
the product life cycle (PLC) theory. In doing so, this dissertation attempts to provide
insights for both managers and researchers on how to make strategic acquisition decisions
for new process technology. This is an important understanding given the current, rapid

diffusion of new process technology.
1.1 OBJECTIVES OF RESEARCH

Faced with fierce and ever-rising competition created by the proliferation of new
products and shorter or truncated product life cycles, many US. manufacturing firms
recently began to adopt new process technology, such as the Just-In-Time (JIT), Robots,

Computer Integrated Manufacturing (CIM), or Flexible Manufacturing System (FMS), to

 

3 The product and technology matrix is a slightly different version of the product-process matrix
suggested by Hayes and Wheelwright [1984]. In this matrix, a process technology choice is determined by
the combination of the degree of automation capacity and the degree of flexibility. The stages of PLC are
represented by the third dimension in the matrix.

 

 

improve manufacturing flexibility. Although the need for investment in new process
technology has been widely recognized by most manufacturing firms as well as
academicians there have been few studies concerning the technology acquisition problem
in the broad context of manufacturing strategy. This study attempts to respond to this
need by exploring the strategic implication of new advanced technology acquisition.
Results of this study are expected to offer managers sound guidance for optimizing
manufacturing systems' performance potential in the long run (Kantrow 1980).

It has been argued frequently that the relatively weak performance of the US.
manufacturing industry during the last two decades has been due partly to an obsession
with short-term performance and to the attention given solely to cost efficiency by most
production managers (DeMeyer et al. 1989; Kaplan 1986). A similar phenomenon can be
observed in past analytic studies. Most of the studies employ cost efficiency as the single
decision criterion. However, the importance of manufacturing flexibility has become
recognized by both practitioners and academicians. In fact, some empirical studies have
revealed that manufacturing flexibility is a key ingredient for the survival of manufacturing
firms in the fierce competition of the international market (DeMeyer, et al. 1989; see also
Buffa 1984; Jaikumar 1986; Goldhar and Jelinek 1983). In addition, manufacturing
flexibility is regarded as one of the competitive priorities as opposed to cost efficiency in
terms of the capabilities of process technology (Hayes and Wheelwright 1984). A firm
with the competitive priority of manufacturing flexibility may focus on improving the
flexibility of its process technology to achieve the economies of scope. On the contrary, a
firm with the competitive priority of cost efficiency may focus on increasing the
automation capacity of its process technology to achieve the economies of scale. Hence,
if the objective of technology acquisition is to enhance a firrn’s overall competitive position
in the market, the decision criteria for optimal performance should reflect the trade-offs
involved in these two conflicting priorities. This view is shared by many researchers

(Kaplan 1983; Jaikumar 1986; Hayes and Jaikumar 1988; Hough 1989) who assert that

 

 

4

the evaluation for new technology acquisition should include its intangible benefits.

As suggested by Kim (1989), the studies in strategy can be placed into three
categories: conceptual, empirical, and analytical. Most studies in manufacturing strategy
will be categorized as either conceptual or empirical. Few studies will be included in the
analytical category. In fact, the analytical study of manufacturing strategy is still in its
infancy. One of the important benefits of analytic study is the high controllability of
variables. This controllability permits the exploration of many hypothetical scenarios in a
given study, which would allow the development of new directions from existing theories.
The above view was supported by Robert H. Hayes during his presentation at the 1988
Decision Sciences Institute National Meeting. Hayes asserted that the study of
manufacturing strategy should move to a new stage in which transportable and practicable
research tools are developed. Many have also argued that analytic tools should be
developed to examine the interrelationships among the various components of
manufacturing strategy (see also Kim 1989; Adam and Swamidass 1989). In fact, a too
simplistic or static representation of a strategic problem such as technology acquisition
may distort the analysis of its full impact on overall performance measures, such as ROI,
market share, total production cost, etc., and cause the decision maker to reject the
proposal. Due to the greater power of computing available with the advancement of
personal computers, numerical solutions to the complex model of optimal control theory,
such as the one used in this dissertation, can easily be attained.

In summary, the objective of this study is threefold. First, it examines the problem
of technology acquisition, taking a broad-based view founded on the conceptual
framework identified from the body of literature in manufacturing strategy. Second, this
study employs realistic settings in the model that consider the trade-offs between two
strategic priorities - flexibility and cost efficiency. Finally, the study develops a dynamic
optimal control model to capture the strategic impact of the acquisition of new advanced

technology on a SBU's performance in the long run. The purpose of the model is to

 

 

5

determine the strategic mix of automation capacity and degree of flexibility so that not
only the SBU's strategic objectives but also its tactical and operational objectives are

fulfilled.
1.2 RESEARCH ISSUES

Strategic justification for investment in new process technology is a complex
problem. The investment in new process technology is related not only to capacity
planning but also to a firm’s competitive position in the market. The introduction of new
process technologies, such as FMS, CHM, and Robots, is argued to promise great changes
in the performance of manufacturing firms and even changes in industry structure
(Jaikumar 1986; Hayes and Jaikumar 1988). In particular, many have asserted that
investment in new equipment and technology is indispensable for improving the flexibility
of a firm (Drucker 1971; Frohman 1982; Goldhar and Jelinek 1983; Hayes and
Wheelwright 1984). Accordingly, this dissertation identifies and examines two key
issues. The first is the relationship between the relative emphasis on flexibility and cost
efficiency and the optimal acquisition of new process technology, given market conditions
of product variety and demand growth along the product life cycle. The second issue is
the effect of the optimal acquisition of new process technology on changes in the process
technology choice within the product-technology matrix. This dissertation explores the

strategic implications of these two issues in a dynamic environment.

1.2.1 Flexibility and Cost Efficiency of New Process Technology

Primarily, this dissertation examines the relationship between a firm's strategic
priorities and the optimal acquisition of new process technology along the stages of the
product life cycle (PLC). Based on the literature review, this dissertation identifies two

major reasons for the lack of strategic perspectives in past analytic studies of the new

 

 

6

process technology investment. The first one is the improper definition of new process
technology, and the second is the failure to address the dynamic interaction between
technology investment and strategic competitiveness variables, such as degree of
automation capacity, level of overall system flexibility, market conditions, and
manufacturing cost structure.

Most past analytic studies for technology acquisition problems define process
technology in units of output (Bustein 1986; Fine and Freund 1986; Gaimon 1985, 1986,
1989, and 1992; Roth et al. 1991). These studies fail to consider the strategic aspect of
new process technology —- flexibility -- and thereby narrow their scope by disregarding the
product variety aspect of market demand. In addition, the models in these studies usually
have an objective function with a single criterion of cost performance. In this dissertation,
however, new process technology is defined as the combination of automation capacity
and flexibility. Automation capacity is defined as production capacity in units of output
substituted for conventional old capacity (see Groover 1980). Also, flexibility is defined
as the capability of the production system to produce a variety of products (parts). Hence,
process technology of a manufacturing system is defined here in terms of both the
automated capacity (degree of automation) and the level of flexibility (degree of
flexibility). The acquisition of new technology, therefore, entails two separate decisions:
the acquisition of automation capacity and the acquisition of flexibility. More
significantly, this new definition of process technology allows the model here to consider
the product variety aspect of market demand as an environmental variable.

Many argue that justifying new technology acquisition based solely on cost savings
is incongruous with the surmounting trend of increasing the strategic importance of new
technology (Meredith and Hill 1987; Hill 1989; see also Kaplan 1983). Some even assert
that the introduction of new process technologies, such as FMS, CIM, or Robots, seems

to promise great changes in the performance of manufacturing firms and even in industry

structure (Jaikumar 1986; Hayes and Jaikumar 1988). In particular, many have asserted

 

 

 

 

7

that investment in new equipment and technology is indispensable for improving the
flexibility of a firm (Drucker 1971; F rohman 1982; Goldhar and Jelinek 1983; Hayes and
Wheelwright 1984). Accordingly, they argue that intangible benefits of new technology,
such as improved flexibility, quality, and delivery dependability, should be considered in
the evaluation process for the acquisition of new technology.

This dissertation maintains that the cost-benefit analysis for new technology
acquisition should be linked to the strategic priority determined by the corporate strategy.
By doing so, the technology acquisition decision can be made consistent with the
objectives of the corporate strategy (Frohman 1982; Garrett 1986; Hayes and
Wheelwright 1984; Skinner 1974). In this dissertation, the acquisition of automation
capacity is assumed to create benefits such as savings in variable costs due to the
elimination of direct labor (Hough 1989; Jaikumar 1986) and improved product volume
flexibility due to increased production volume capacity. The acquisition of flexibility is
also assumed to yield benefits of improved product mix flexibility. However, new
technology acquisition accompanies enormous fixed costs of capital investment (see Miller
1985; Smith et al. 1986).. Therefore, it can be conjectured that if a firm's strategic priority
is cost efficiency, the firm would want to invest less in new process technology than a firm
with a strategic priority of flexibility. Hence, the acquisition decisions for new technology
have to consider the proper balance between the fixed investment costs of new technology
and its long-term benefits in order to be consistent with the firm’s choice between cost
efficiency and flexibility as its strategic priority.

Accordingly, the model developed in this study considers both cost efficiency and
flexibility in the objective fiinction as two conflicting criteria measuring the long-term
performance of a manufacturing firm's SBU. Specifically, the strategic priority of a firm is
reflected by the values of cost coefficients assigned to measure flexibility in the objective
firnction. Through the scenario analysis using the model, this study attempts to explore

the effects of different strategic priorities on the optimal acquisition of new technology by

 

 

 

8

changing the values of those cost coefficients. In summary, this dissertation extends the
analytic models of past studies by defining new process technology with two separate

variables —- automation capacity and flexibility -- and attempts to identify the relationship
between the relative emphasis placed on two competitive priorities -- cost efficiency and

flexibility -- and the optimal acquisition of new process technology.

1.2.2 Acquisition of New Process Technology and Evolution of Technology Choice

According to the normative concept of the conventional product—process matrix
theory (Hayes and Wheelwright 1979a), the best "fit" on the matrix is determined by
identifying a technology choice which has the optimal operating characteristics for a
particular market demand in volume and product (part) variety. One major drawback of
this conventional matrix theory is the definition of technology choices identified along the
vertical axis of the matrix. This drawback is due to the fact that both automation and
flexibility are considered in a single dimension. This theory views each technology choice
with a fixed setting, such as a highly flexible system with a low degree of automation (Job
Shop) and, conversely, a highly automated system with a low degree of flexibility
(Transfer Line). However, it does not consider process technologies with any
combination of both flexibility and automation such as a highly automated system with a
high degree of flexibility.

As Hayes and Wheelwright (1984) admitted, the product-process matrix is no
longer congruous with the current trend of rapid diffusion of new process technologies
which have the characteristic of high degrees of both automation and flexibility.
Recognizing the inadequacy of the conventional product-process matrix, this study
proposes a new matrix called a "Product-Technology Box” in which a technology choice
is defined in terms of both the degree of automation along the horizontal axis and the
degree of flexibility along the vertical axis. In this box, an additional third dimension is

included to represent the dynamic environment of the product life cycle (PLC), reflecting

 

 

 

9

the changing market requirements in terms of both product volume and variety as does the
horizontal axis of the product-process matrix.

This study also recognizes the recent findings of the "Reversal of the Traditional
PLC" ( The Reversed PLC), in which demand for both product volume and variety
continue to increase after the grth stage without making a transition to the maturity
stage (Abernathy et a1. 1983; Ayres and Steger 1983). Accordingly, the third dimension
of the new box can be defined by either the Normal PLC or the Reversed PLC. The
premise linking a technology choice and a stage of the PLC in the new box is the same as
in the conventional product-process matrix. The process technology choice determines
both the manufacturing cost structure and the variety of either products or parts the
production system can handle (Hayes and Wheelwright 1984; see also Hill 1989).
Meanwhile, market demand for a particular combination of volume and product variety at
a certain stage of the PLC requires the proper operating characteristics of process
technology. Therefore, this dissertation posits that the best position for a technology
choice in the new box is different, depending upon the type of the PLC.

The analysis in this study also considers the interaction between the firm's strategic
emphasis on a particular competitive priority and the actual changes in the future market
environment, such as aggregate market demand changes and the introduction of new
products. For example, Firm A, whose strategic priority is flexibility, may purchase new
process technology to improve flexibility. On the contrary, Firm B, whose strategic
priority is low cost, may be reluctant to purchase new process technology unless savings
from new investment is greater than the fixed investment cost. Since improved flexibility
due to the acquisition of new process technology by Firm A may reduce the operating
costs by the efficient use of materials, elimination of manual activities, and so forth, its
overall cost performance may surpass Firm B's cost performance in the long run.
However, the performance of these two firms may be affected by changes in the market

environment such as demand volume changes and product mix changes. If future market

 

 

 

10

demand in volume grows quickly with little introduction of new products, it may not be
appropriate for Firm A to assume a burden of excess investment costs for unnecessary
flexibility. On the other hand, if the future market demand for product variety increases
steadily, Firm A will enjoy the advantage of its flexibility while Firm B may lose its
competitiveness in the long run.

In summary, using a dynamic optimal control model, this dissertation explores the
difference in the evolution of technology choice under the different market environments
encountered in two types of product life cycle. In addition, it explores how the
technology choice evolves in the new product-technology box according to the interaction
effect between changes in the firm's external market conditions and changes in its relative

emphasis on two competitive priorities -- flexibility and cost efficiency.

1.3 RESEARCH QUESTIONS

Based on the issues discussed in the previous section, this section sets forth
research questions for this study. According to the discussion in the previous section, the
research questions are divided into two categories: (1) the acquisition policy for new
technology and (2) the evolution of process choice. There are also subordinate research

questions related to the major questions in each category.

1.3.1 Questions for The Acquisition of New Process Technology
The major question for the acquisition of new process technology is, "How do the
optimal acquisition policies for the new technology of a firm differ according to the choice
of its strategic priority ?" Related questions include:
a) Does a firm purchase more new technology when its strategic priority is
flexibility compared to other priorities?

b) In the long run, can a firm with a competitive priority of cost efficiency

 

 

 

11

achieve a lower cost performance compared to a firm placing primary emphasis on

flexibility?

1.3.2 Questions for The Evolution of Technology Choice

1. One major question concerns how the evolution of process technology choice
through the acquisition of new technology is affected by changing market conditions. For
example, for a product life cycle characterized by a sustained growth stage and an
increasing number of new products instead of the transition from a growth stage to a
mature stage in the normal product life cycle, how does the evolution of technology
choice relative to this new product life cycle differ from that of the traditional product life
cycle?

2. Another major question is, ”How do changes in market conditions interact with
a firm's strategic (competitive) priority to affect decisions involving the evolution of
process technology choice?" Related questions include:

a) For each of the two PLC types described in section 1.2.2, how does the
evolution of technology choice differ due to different choices of strategic priority?

b) Which choice of strategic priority is most insensitive to changes in market

conditions?
1.4 SUMMARY

In this chapter, the fundamental theme of this dissertation is described. In this
dissertation, the problem of new process technology acquisition is evaluated analytically
within the context of manufacturing strategy. Two critical issues are identified. First, this
study explores the effect of a firm's relative emphasis on two competitive priorities -—
flexibility and cost efficiency -- on the optimal acquisition policy for new process

technology. Second, the study explores the interaction effect of strategic priority and

 

 

12

external market environmental changes on the evolution of process technology choice
along the stages of PLC.

This study deve10ps a dynamic based on the optimal control theory, which
considers two conflicting objectives -- maximizing flexibility and minimizing cost
efficiency. With factor settings identified in the body of literature, a scenario analysis is
conducted to examine the research questions. The results are discussed to find possible

extension of and new directions from the existing theories of manufacturing strategy.

 

 

CHAPTER II
LITERATURE REVIEW

This chapter reviews the current body of knowledge related to both manufacturing
flexibility and new advanced process technology. The major purpose of the literature
review is to identify the relevant issues to fill the gap between the current body of
knowledge and the desired path of future researches in this field. It is also to identify the
relevant variables and concepts built in the theories of manufacturing strategy to extend
the researches of the past into a new direction. This chapter reviews the literature related
to the research questions raised in Chapter I as well as the research methodologies for the
acquisition of new advanced technology.

Section 2.1 reviews the past researches for the definition and the conceptual works
of manufacturing flexibility. Through the review, it is possible to identify the definition as
well as the measurement of manufacturing flexibility relevant to the research context of
this study. Section 2.2 is devoted to review the past researches on the process technology
and the product life cycle theory related to the manufacturing strategy. This section
allows to identify the gap between the conventional normative consensus applicable to the
old operating environment of manufacturing firms and the new perspectives of theory
building under the current trend of the operating environment faced by today's
manufacturing firms. The major contribution of the literature review in this section is to
provide the theoretical background to develop the conceptual frameworks in Chapter III,
as the main focus of this dissertation, which links the corporate strategy and the
acquisition decision of new advanced technology.

Section 2.3 reviews the past researches for the manufacturing automation. Again,

13

 

 

14

it attempts to reveal the proper definition of new advanced technology based on the
current body of knowledge. This section also provides contributions in developing the
conceptual frameworks in Chapter III by identifying the interrelationships among the
attributes of new advanced technology and the manufacturing firm's internal and external
operating environment. At the end of this section, several analytical research approaches
taken by the past studies are reviewed to help developing a new analytic model in Chapter

IV based on the conceptual framework developed in Chapter III.
2.1 MANUFACTURING FLEXIBILITY

Flexibility is one of the key objectives of any manufacturing system (Chatterjee et
a1 1984). Hayes and Wheelwright (1984) also considers flexibility as one of the
dimensions of the competitive strategy of a business along with price(cost), quality, and
dependability. Some researchers contend that flexibility is the next battle ground in
international industrial competition (DeMeyer et al. 1989; see also Stalk 1988). Baranson
(1983) argues that the global view held by Japanese companies towards marketing and
production explains why Japanese managers there take a long-term and comprehensive
view towards capital investments that consider not only cost savings but more significantly
the broader strategic implications of increased flexibility.

There are many different versions of the definition of manufacturing flexibility.
The dimensions of manufacturing flexibility defined by numerous studies include such
factors as volume, mix, machine, design, operation, process, and design. This section
reviews the literature on flexibility to identify the proper measure of flexibility as the firm's
performance. This section focuses on two key dimensions of manufacturing flexibility —-
product mix flexibility and volume flexibility -- which have been regarded as major

competitive priorities closely related to manufacturing flexibility at the strategic level.

 

 

15

2.1.1 Definition of Flexibility

Although there are many different versions of the definition of flexibility, the term
"manufacturing flexibility" is not well understood (Kumar 1986; Swamidass 1988). The
general issue of flexibility can be traced as far back as the early 19205. In an economic
context, flexibility was defined as "the risk arising from the immobility of invested
resources" (Lavington 1921), "a relatively flat average cost curve" (Stigler 1939), and "the
inverse relationship with the marginal cost" (Nelson 1962). Flexibility has also been
examined in the Organizational Behavior literature, e. g, organizational flexibility
(Feibleman and Friend, 1945), structural flexibility (Preece, 1986), or labor flexibility
(Atkinson 1985).4

In the context of manufacturing, flexibility is defined as "the ability to respond
effectively to a wide range of possible environments" (Gerwin 1987; Sethi and Sethi 1990;
Slack 1983; Zelenovic 1982). Some define it in terms of resource capability, some in
terms of constraints on manufacturing environment, and others in terms of both internal
and external environmental uncertainties. In view of capability, Sethi et a1. (1990) defines
manufacturing flexibility as the "ability to reconfigure manufacturing resources so as to
produce efficiently different products of acceptable quality." Jaikumar (1984) asserts that
manufacturing flexibility should be constrained within a domain defined by the portfolio of
products, process, and procedures and the domain should be planned, managed, and with
learning expanded. With regard to environmental uncertainties, Garrett (1986) suggests
that manufacturing flexibility is required in order for a firm to cope with both internal
changes and external forces. The internal disturbances for which flexibility is useful
include equipment breakdown, variable task times, queuing delays, rejects, and rework
(Buzacott and Mandelbaum 1985). External forces refer largely to the fiindamental
uncertainties of the competitive environment (Behrbohm 1985; Zelenovic 1982; Garrett

1986; Maier 1982).

 

4 For the details of these definitions, see Sethi and Sethi (1990).

 

 

 

16

Kim (1989) defines manufacturing flexibility at three different levels, strategic,
tactical, and operational. According to his typology, manufacturing flexibility at the
strategic level reflects environmental uncertainties (see also Kamarkar and Kekre 1980;
Zelenovic 1982; Slack 1983). At the tactical level, manufacturing flexibility is recognized
as a concept conflicting with efficiency (see also DeMeyer et al. 1989). Here,
manufacturing flexibility is viewed in terms of trade-offs between economies of scale and
economies of scope (see also Cohen and Lee 1985 ; Goldhar and J elinek 1983). Kim
points out that both product mix and volume flexibility are identified as two competitive
priorities closely related with manufacturing flexibility in both the strategic and the tactical
levels. Kim argues that these two flexibilities well reflect the strategic priorities imposed
by a firm's external environments (see also Swamidass 1986). Two of the main external
uncertainties faced by manufacturing firms will be the demand changes and the
introduction of new products in the market. Accordingly, Kim asserts that the response to
these uncertainties is properly captured by the definition of these two flexibilities. Finally,
at the operational level, manufacturing flexibility is recognized as operational capabilities
provided by a particular process technology (see also Browne et al. 1984).

According to the literature, the definitions of manufacturing flexibility in the
strategic context usually imply "responsiveness to uncertainties." Sethi and Sethi (1990)
states "The probabilistic nature of these uncertainties may not always be known.
Uncertainties may exist for level of demand, product prices, product mix, and availability
of resources. Uncertainties may arise out of actions of competitors, changing consumer
preferences, technological innovations, new regulations, etc.” Hence, manufacturing
flexibility has major implications for a firm's competitive strength, and this significant role
of manufacturing flexibility makes it a part of the firm's strategy. In this regard, by
recognizing demand changes and the variety of new products as two major external
uncertainties, this dissertation includes both product mix flexibility and volume flexibility

as key competitive priorities of a manufacturing firm in coping with these uncertainties.

 

 

17

2.1.2 Manufacturing Flexibility and Its Strategic Implications

As stated above, there are many dimensions of flexibility identified in the literature.
Even the definition for each dimension of flexibility differs among authors. This section
traces the proper definitions of product volume and mix flexibilities for the study in this
dissertation. Furthermore, manufacturing flexibility entails not only the direct benefits of
quick and economic response to environmental uncertainties but also other indirect
benefits that might be overlooked. Thus, this section explores the strategic implications of

flexibility for a firm's competitiveness in the long run.

2.1.2.1 Definition of Product Mix Flexibility and Volume Flexibility

In this dissertation the main theme is characterized as the strategic analysis of
process technology. Accordingly, product mix and volume flexibilities will be defined in
the context of manufacturing strategy. In the literature of manufacturing strategy these
two flexibilities have been frequently referred as primary types of flexibility at the strategic
level (Cox 1989; Kim 1989; Schonberger 1986; Skinner 1974).

Product mix flexibility is "the ability to respond inexpensively and rapidly to
additional or substituted products" (see also Gupta, et a1. 1989). Sethi and Sethi (1990)
defines product mix flexibility as the ease with which new parts can be added or
substituted for existing parts. This definition is very similar to the definition of product
flexibility of Browne et al. (1984), part flexibility of Gerwin (1982) and Falkner (1986),
and design adequacy of Zelenovic (1982). According to these definitions, product mix
flexibility is referred as "the capability of process technology to respond to part changes
or part addition." It is important to note that in Lim's survey of FMSs in the United
Kingdom (1987), 11 out of 12 reporting companies considered manufacturing flexibility to
mean product mix flexibility.

Product volume flexibility is defined as "the ability to operate profitably at

 

 

18

different overall output levels” (Gupta, et a1 1989, Sethi, et al. 1990). Similar definitions
can be found in other studies (Browne et a1. 1984; Gerwin 1982; Maier 1982; Kegg 1984).
This definition is also similar to the definition of demand flexibility by Son and Park
(1987). According to Slack (1987), volume flexibility has two aspects: speed of response
and range of variations. He suggests that the former is usefiil in the short term while the
latter is useful in the long run.

Buzacott (1982) defines flexibility with some strategic flavor. He separates
flexibility into job and machine flexibility considering the nature of the change and
disturbances that the system should be able to cope with. Job flexibility is defined as "the
ability of the system to cope with external changes such as the type, mix, processing
requirements, and quantity of jobs allocated to the system," while machine flexibility is
related to the internal changes such as machine breakdowns, variability in processing
times, and quality problems. According to his definition, both product volume flexibility

and product mix flexibility reflect his definition of job flexibility.

2.1.2.2 Strategic Value of Manufacturing Flexibility

According to a recent research, conducted by The Manufacturing Roundtable of
Boston University (Miller and Roth 1988), flexibility was ranked from fourth to eighth in
importance for future competitiveness, and first in the size of strategic gap (i.e., the
difference between current capability and firture needs). However, flexibility did not
appear at all in a list of 10 key performance measurements offered by the responding
executives. The authors give two primary reasons for this discrepancy. First, in contrast
to cost, delivery, and quality, which have been the cornerstones of manufacturing planning
and control for many years, the idea of flexibility as a top priority has only recently come
to the fore. Consequently, it tends to be treated, even on a conceptual level, less ofien and
usually on a somewhat abstract as opposed to concrete basis. Second, and partly because

of the first, the technology for measuring flexibility is poorly developed (see also Cox

 

 

19

1989)

On the contrary, Baranson (1983) argues that the global view held by Japanese
firms towards marketing and production explains why Japanese managers take a long-term
and comprehensive view towards capital investments considering not only cost savings in
labor, material, and space, but more significantly the broader strategic implications of
increased flexibility and versatility in designing and producing of products (see also De
Myer et al. 1989). Empirical evidence also supports the view that flexibility does not get
its proper due at the time of decision making with regard to investment in manufacturing
technology (Lim 1987). Hence, it is critical for the evaluation of technology investment to
consider the long term strategic implications of enhanced flexibility due to the acquisition
of new technology.

The current trend of market and industry structure is being shaped by the
adaptation of new advanced manufacturing technologies, rapid-response systems to
environmental uncertainties and complexity, expanding variety and increasing innovation.
The competitive values of manufacturing flexibility lie in its ability to neutralize the effects
of demand uncertainties (Swamidass 1986; Swamidass and Newell 1987), to increase
market share through the proliferation of new products, and to achieve low cost
production with the economies of scope (Hill 1988). Stalk (1988) asserts that companies
adopting strategies based on flexible manufacturing are reducing if not eliminating delays
and using their response advantages to attract the most profitable customers. By
characterizing the flexible firm as a time-based competitor, Stalk further asserts that the
flexible firm achieves lead time reduction with small lot size and, hence, enjoys big
advantages in both productivity and time: labor productivity in time—based factories can be
as much as 200 percent higher than in conventional plants; time-based factories can
respond eight to ten times faster than traditional factories. For example, Toyota had its
suppliers reduce production lead time from 6 days to 1 day by reducing lot sizes and the

number of inventory holding points (Stalk 1988).

 

 

 

 

20

In particular, a firm with high product mix flexibility can simultaneously pursue
both a differentiation and a low cost strategy (Hill 1988). High product mix flexibility can
allow the firm to introduce more products to the market than can its competitors. In the
long run this will increase the brand loyalty of its customers and, consequently, increase
the demand (see Roth et al. 1991). Hence, the firm can achieve the objective of the low
cost strategy due to the economies of scale with the increased demand. An empirical
study by White (1986) shows a significant and positive relationship between differentiation
and low cost (see also Phillips et al. 1983). White found that 19 of 69 business units had a
competitive advantage based on a combination of both differentiation and low cost.
Moreover, his result suggests that business units that successfully combined both low cost
and differentiation had the highest return on investment.

The strategic values of both product volume flexibility and mix flexibility referred
in the literature can be summarized as follows: first, the ability to neutralize the demand
uncertainties; second, lead time reduction; third, low cost production; finally, increased
market share. The latter two values explain how firms can pursue both a differentiation

strategy and a low cost strategy simultaneously.

2.1.3 Measurement of Manufacturing Flexibility

Since the survival of manufacturing firms will depend on their ability to adapt to
rapid change in the market and industry, flexibility plays a key role not only as a strategic
goal but also as one of the important means for performance measurement (Gupta and
Goyal 1989) reflecting the changing source of competition (Stalk 1988). However, since
flexibility, as Slack (1983) points out, is an indication of potential it is difficult to measure
(see also Son and Park 1987). This is why there are so many different definitions of
flexibility. Before defining the measurement of flexibility it is important to identify the key
elements that constitute it. Another important aspect of measurement is how to measure

the manufacturing system's overall flexibility as a performance in the dynamic production

 

 

 

 

21

environment.

There are two major elements in measuring manufacturing flexibility identified in
the literature, time and cost. Gupta and Goyal (1989) states that manufacturing flexibility
has been measured by cost, delivery speed, or lead time (see also Cox 1989). Particularly,
product mix flexibility can be measured by time or cost required to switch from one part
to another, not necessarily of the same part types (Browne et al. 1984; Buzacott 1982;
Zelenovic 1982). Son and Park (1987) suggests a measurement of product mix flexibility
as the ratio of the physical output of the system to the setup cost of the equipment,
viewing the reduction in setup costs as the way to increase product mix flexibility.

As a generalized measure developed from the definition of product volume
flexibility of Browne et al. (1984), Gupta and Goyal (1989) suggests "the range of
volumes in which the firm can run profitably. " Gerwin (1987) measures it by "the ratio
of average volume fluctuations over a given period of time to the production capacity
limit." Falkner (1986) suggests, as a measure of volume flexibility, "the stability of
manufacturing costs over widely varying levels of total production volume." Son and

Park (1987) suggest measuring it with "the ratio of the physical output to the inventory
cost of finished product and raw material," indicating the difference between supply and
demand, that is, response to internal and external demand.

Although a single comprehensive measure of manufacturing flexibility takes into
account its direct benefits such as quick and economic response, it should be carefirlly
designed with a broad perspective since the benefits of flexibility are also reflected
indirectly by the improvement in other performances, such as inventory cost reduction or
improved market position. In the past literature, there are few examples of using a single
quantified measure of manufacturing flexibility in researches on manufacturing strategy.

Nagarur (1992) presents a simple approach to measuring the flexibility of FMS. He
introduces an index, which is called "producibility," which measure the reroutability of any

excess load of a machine center to other centers. This flexibility index may be useful in

 

 

 

22

measuring the overall operational flexibility of production system. A recent study by
Ramasesh et al. (1992) employs the ratio of the net revenue generated by the system to the
standard deviation of the net revenue distribution as a measure of aggregate flexibility. In
their mixed integer stochastic program, the objective function generates ’optimal
production levels for a set of products to maximize the net revenue considering costs
associated with product mix and product volume flexibilities. Beside some conceptual
drawbacks of this particular model, it is hard to expect that the solution of the model may
guide any significant strategic implications, rather it may be suitable for a comparison
purpose for different systems under a static environment.

Relevant to the study of this dissertation, Gupta and Goyal (1989) suggests that a
single measure for manufacturing flexibility should be concomitant with a given
manufacturing strategy. Then, the manufacturing system could be designed and modified
accordingly. Importantly, it is necessary to develop a measure of overall manufacturing
flexibility under a specific production setting of process technology and production control
system, considering the economic consequences of future environmental changes (see
Buzacott 1982). As Gupta and Goyal (1989) asserts that flexibility is not a self-contained
concept, it may be necessary to relate manufacturing flexibility as a performance measure
to the other production objectives related to manufacturing cost and time such as
operating cost and lead time. Hence, Gupta and Goyal imply that the system's flexibility
will be reflected in the long term performance measured in both operating cost and lead
time through constant changes in process technology and control system according to the

environmental changes.

2.1.4 Trade-off between Flexibility and Efficiency
The conflict between flexibility and cost efficiency primarily account for by the
high initial investment of new technology which enhance flexibility. For example, a

numerical controlled turning center costs $300,000 to $400,000, whereas an FMS

 

 

 

23

installation can balloon to $25 million (Bobrowski and Mabert 1988). For this reason,
flexibility and cost efficiency have been considered as conflicting objectives (De Meyer et
al. 1989). By defining flexibility with two conflicting factors, "Quick Response to
Change" and "Economic Response to Change," Chung and Chen (1989) conceptualize
flexibility as the compromise between these two factors. They provide a simple formula
for conceptualizing a framework for flexibility evaluation,

The Total System Flexibility = orQ + (1- or)E,
with or for a weight, Q for a factor of the quickness, and E for a factor of the economic
response.

However, as discussed before, a firm with high flexibility may pursue successfully
both differentiation and low cost strategies simultaneously. In particular, a firm with high
product mix flexibility can introduce more new products into the market than can its
competitors and, consequently, enjoy the economies of scale through the increased market
share (Hill 1988). Hill (1988) suggests that, if the cost reduction due to the increased
market share outweighs the increase in the cost related to product variety, the firm can
pursue successfirlly both differentiation and low cost strategies at the same time. In fact,
some empirical studies (Newell and Swamidass 1987; Meyer et al. 1989; Phillips, et al.
1983; White 1986) provide evidence that there is a positive relationship between MF and
other manufacturing performance measures such as cost reduction and/or quality
improvement.

In summary, the cost implication of manufacturing flexibility entails two aspects.
On one side, flexibility will increase the cost due to the huge initial capital investment and
the potential increase in variety of products. On the other side, however, flexibility will
reduce the cost due to the efficient use of labor and materials and the increased market
share resulting from a firm's enhanced competitive position in the market. Hence, in
deve10ping a model for the study of flexibility it is important to consider not only the fixed

investment cost in improving flexibility but also the other benefits related to cost reduction

 

24

due to the improvement in flexibility.

2.1.5 Summary and Implications

It seems that the flexibility of advanced process technologies can become easily
submerged in technical requirements, thereby disregarding a holistic view (Sethi, et al.
1990). This View is consistent with the result of an empirical study by Slack (1987) in
which the managers are found to focus more on flexibility of individual resources than on
the flexibility of the production system as a whole. It is also found from the literature that
manufacturing flexibility has a significant long-term strategic impact by enhancing a firm's
competitive position in the market. However, the literature concerning manufacturing
flexibility needs empirical studies with raw data from industries to identify the functional
relationships among the means of achieving manufacturing flexibility such as investment in
new process technologies and manufacturing parameters such as lead time, fixed and
variable costs. These studies are indispensable as research background to aid the
conceptual and theoretical studies in analyzing the strategic implications of investment in
new process technologies on the firm's competitive position in the market.

In summary, the implication for this dissertation can be summarized in three major
points. First, both product mix flexibility and product volume flexibility, as performance
measures for the analysis of strategic issues, are found properly related to and should
reflect the contingencies of the corporate strategy. Second, manufacturing flexibility is
found to have a significant impact on a firm's market position with potential market share
growth and the cost structure of the firm's production system. Finally, it is also found that
the variables related to both manufacturing time and cost representing the long-term

effects of flexibility should be used for strategic evaluation of manufacturing flexibility.

25

2.2 PROCESS TECHNOLOGY AND PRODUCT LIFE CYCLE

The first part of this section will be devoted to exploring the literature related to
process technology. The purpose of the review is to find, first, how the past studies
categorize different process technologies as defined in the product and process matrix
theory (Hayes and Wheelwright 1984) and, second, how this categorization scheme can be
applied as a theory of manufacturing strategy in the emergence of new advanced process
technologies. The current trend of the international market, characterized by a great
variety of custom-made products, shorter or truncated product life cycle, and the
expectation of higher standard by customers, is quite different from the conditions in the
past, which the theory has been constructed from and applied to. Furthermore, the greater
emphasis on flexibility in every aspect of manufacturing process also steers the strategic
priority from cost efficiency to flexibility. With the notion of these changing market trends
and strategic perspectives, it would be necessary to revisit and reevaluate the conventional

theory of manufacturing strategy.

2.2.1 Process Technology in Manufacturing Strategy

Technological change is not important for its own sake, but it is important if it
affects competitive advantage and changes industry structure as asserted by Porter (1980).
It plays a key role in linking the manufacturing capabilities which it embodies and
marketing requirements and, consequently, it determine the company's competitive
position in the market. Simmonds (1981) also states, while pointing out the drawbacks of
the conventional accounting approach of investment appraisal, that new production
investment must imply a change in competitive position and it is this change that should be

the focus of the investment review.

 

26

2.2.1.1 Characteristics of Process Technology

There are many studies related to the definition of process technology (Buffa 1984;
Hayes and Wheelwright 1984; Hill 1989). Buffa (1984) identified four different process
technologies as physical systems in positioning strategy -- a product-focused system for
high volume, a product-focused system for multiple products in moderate volume, a
process-focused system for moderate to low volume, and a process-focused system for
custom products. The definition of these processes is not much different from the
conventional definition of job shop, batch, line, and continuous processes (Hayes and
Wheelwright 1979a). The distinction among these four process technology has been made
by the difference in two key aspects -- degree of flexibility and level of investment in hard
automation. Buffa explains the rationale for the investment in hard automation as a mean
of achieving low cost objectives with high volume products. Buffa also relates flexibility
to the product mix to distinguish those four systems. Hence, Buffa identifies flexibility

and automation as key dimensions of process technology.

Table 2.1. Characteristics ofProcess Choice

 

 

Process Choice Process Automa- Overhead Direct Direct
flexibility tion cost labor Material

Job Shop High Flexible Low High Low

Batch

Assembly

Continuous Low Hard High Low High

 

 

 

 

Source: Buffa (1984) and Hill (1989)

A detailed description of the characteristics of these process choices is provided by
Hill (1989). In Table 2.1, an anecdotal summary is presented for some of key

characteristics of process choices extracted from two studies (Buffa 1984, Hill 1989).

 

 

 

27

These studies are related to the study of this dissertation. As shown in Table 2.1,
compared to continuous process, job shop process has higher process flexibility. In
general, job shop process employs flexible technology, whereas continuous process
employs hard automation to lower the variable production cost. In terms of cost
structure, job shop tends to have a relatively low fixed cost compared to high variable
cost, whereas continuous process tends to have high fixed cost compared to low variable
cost. The different cost structures of the four process types are mainly due to the natures
of the process technologies they adopt. Hill (1989) maintains that job shop process
involves the high content of direct labor, thus making it the highest portion of total costs.
On the contrary, due to the high process investment, direct labor costs for continuous
process are small and its site/plant overheads are high owing to the need to support the
process and handle the high output levels involved. However, an empirical study by Raffr
and Swamidass (1987) shows that the ratio of manufacturing overhead costs (MOHC)5 to
total manufacturing cost is not significantly different among process choices. There are
not many studies dealing with the process choices in the past. Nevertheless, it is generally
found that the process choices are usually characterized through the relative comparison in
terms of cost and flexibility (Buffa 1984; Hayes and Wheelwright 1979a; Hill 1989; Raffr

and Swamidass 1987).

2.2.1.2 Process Technology in Capacity Planning

One of the critical issues in the studies of the acquisition of new technology is the
pattern of change, frequently called as timing -- gradual transition, incremental, from old
to new technology, or upheaval, radical, of old technology. Yet, there is no clear—cut

conclusion on this issue drawn from the literature. Gaimon(l985) in a study of the

 

3 The major component of MOHCs include, indirect supplies, indirect labor, supervisory salaries,
social security taxes, pension payment, health care costs, overtime premiums, idle time costs. vacation
pay, depreciation, property taxes, property insurance, repairs and maintenance, power costs. and material

handling.

 

 

 

28

optimal acquisition of automation supports the incremental changes of technology.
Gaimon used this view as an assumption in developing her model in the study of the
optimal acquisition of automation (see also Roth et al. 1991). On the contrary,
Vickson(1985) found in his study that the optimal transition from old to new technology
has to be instantaneous. He drew his View from the conclusion in the study of the
investment choice between two production technologies. Unfortunately, this conclusion is
almost predictable from one of the constraints in his model.

Skinner (1974) also points out that the incremental change in production system
and organization structure as one of the major factors causing confusion and inconsistency
in making strategic decisions and leading to poor performance. Since both Vickson's and
Gaimon's models are too simple, missing the dynamic relationships among many important
aspects of manufacturing system, it may not be appropriate to draw any conclusion from
their studies. Even though Skinner's assertion is based on broader perspective than
Vickson's, it does not reveal the details of strategic implication of investment timing in the
process of manufacturing strategy formulation.

The other critical issue is related to the evaluation criteria of technology
investment. As Hill(1989) points out, the excessive use of ROI distorts strategy building
(see also Madu and Georgantzas 1991). He suggests that only when a company reviews
investment in light of its corporate strategy, marketing strategy, and manufacturing
strategy, the essential cohesion will be established. Until this happens, the company will
be in danger of investing in ways that will not give the necessary synergetic gains of
strategic coherence. Hence, the evaluation criteria for technology investment should be
concomitant with the congruent objectives of those strategies. Particularly, evaluation
criteria should be associated with the firm's competitive priority based on the forecast of

the contingencies of the firm's operating environment.

29
2.2.1.3 Process Technology in Manufacturing Strategy

Good process technology strategy can be characterized by fit or consistency,

which ensures that the firm's process technology evolves in a directed fashion, so that as

 

technological capabilities are renewed and augmented, they reinforce and expand the firm's
competitive position (Hayes and Wheelwright, 1984). This fit can be understood by
identifying general capabilities of process technology not only with existing products but
also with new products to be introduced and the changes to the existing products in the
dynamic environment. (Hayes and Wheelwright, 1984). Hence, the analysis of process
choice for a firm should be carried out with the understanding of the dynamic changes of
the firm's internal and external operating environment.

As discussed in the previous section, another important observation in past studies
of the evaluation of process technology is criticism of the use of a single performance
criterion (Kaplan 1983; Simmonds 1981). Many of those earlier studies analyze their
problems with a single criterion of cost minimization or profit maximization by including
variables defined with narrow and abstract perspectives.6 As a result, most of these
studies fail to characterize adequately the nature of different process choices that are
frequently referred in manufacturing strategy literature and, consequently, fail to address
the relevant strategic issues related to the acquisition of new advanced process
technology.

In particular, more researches is called for in the area of identifying key strategic
variables affected by the adaptation of new advanced technology. An effort by Ettlie et al.
(1984) shows empirical results identifying the organizational causality of two different
innovation processes, radical and incremental. What is needed for the strategic evaluation
of new advanced technology is empirical study such as this to identify the critical
organizational factors in the causal relationships between decision variables related to the

acquisition of new advanced technology -- timing and size -- and a firm's overall

 

6 The detailed review of these studies will be presented later in section 2.3.3.

 

30

performance including not only the strategic but the operational.

2.2.2 Product Life Cycle in Manufacturing Strategy

2.2.2.1 Product Volume and Product Mix in the Product Life Cycle

Key elements of the conventional product life cycle relevant in manufacturing
strategy are product volume and product mix. Four distinguished stages -- introduction,
growth, maturity, and decline -- are characterized by the different ranges of product
volume and product mix. Hayes and Wheelwright (1984) summarize the framework of
Wasson (1978) with four dimensions that are directly linked to manufacturing: production
volume, product variety, industry structure, and the dominant form of competition. They
suggest that

while the product life cycle is usually primarily in planning a firm's marketing strategy,

it also has great implication to the firm's manufacturing strategy. For example,

manufacturing has a major stake in decisions that may affect such variables as product

customization (versus standardization), volume per model, and the average time before

obsolescence or replacement. Given this perspective, the product life cycle can be used

to summarize the customer and product requirements that must be satisfied by the

manufacturing function and its production technology.

They also assert that, by identifying the relevant range of product volume and
variety for each stage of the product life cycle, the stage can be matched to the
corresponding stage of the process life cycle according to the capability the production
system can provide. As a result, they developed the famous "product-process matrix."
Hill (1984), in his conceptual framework called "product profiling," also related the
relevant range of volume and variety to process choice as one of the many operational
aspects as a best fit between the marketing strategy and the manufacturing strategy. Even
though the concept of the PLC theory has been well accepted in the area of POM until
now, the concept of the traditional product life cycle has been challenged by some

researchers in marketing area (Ayres et al. 1983; Dhalla et a1 1976). Particularly, Ayres

and Steger ( 1983) asserts that, with the rapid evolution of technology, the product life

 

 

31

cycle can be reversed.

2.2.2.2 Manufacturing Cost in PLC

Stalk (1988) suggests that manufacturing costs fall into two categories: those that
respond to volume or scale and those that are driven by variety. He reports that scale-
related costs decline as volume increases; usually falling 15 percent to 25 percent per unit
each time
volume doubles. Variety-related costs, on the other hand, reflect the costs of complexity
in manufacturing: setup, material handling, inventory, and many of the overhead costs of a
factory. In most cases, as variety increases, costs increase, usually at a rate of 20 percent
to 35 percent per unit each time variety doubles. Stalk suggests that the optimum cost

point for factories can be decided by the combination of volume and variety (see figure

2.1).

 

+ Scale-related
Costs

+Variety-related
Costs

+Total Cost of

Operation

 

 

    

 

Product Cost

 

Low Mediu High
m

 

Volume, Variety

Figure 2.1 Variety-Related Costs and Scale-Related Costs

An empirical study of metalworking industries by Ayres and Miller (1981) shows

evidence that the unit production costs increases as the batch size increases. The study

 

32

estimates that the unit costs are about ten times higher for custom production in a job
shop environment than they are for mass-production operations. It also estimates that the
unit costs in the intermediate batch processes range from three to five times higher per

pound of material processed than in the most extreme mass production case.

2.2.2.3 Reversal of PLC

In contrast to the traditional product life cycle concept, Ayres and Steger(1983)
suggest the reversibility of the biological evolution of the product life cycle. They argue
that a "product" may not move through the cycle from birth to death in one direction only
and there are a number of historical examples of reversals in the sequence, attributable to
technological changes that altered the product significantly but did not replace it. Ayres
and Steger also suggest three conditions for the reversal of the product life cycle are
potential for accelerated technological change, management flexibility, and manufacturing
flexibility. The authors also argue that the evolution process can be accelerated or
decelerated depending upon a firm's competitive advantage. If a firm is competent in mass
production, it will be beneficial to accelerate the product standardization and maturity. On
the contrary, if a firm has competence in technological flexibility, then it will be safe to
adopt the deceleration strategy (see also Tombak 1988).

Abernathy, et al. (1983) call this reversal of the life cycle "dematurity" in their
framework of the product life cycle. They argue that major technology changes can throw
an industry back into a growth stage. After close examination of the US auto industry, the
authors found that the auto industry served as a prime example of this process of "de-
maturation." The immature standardization efforts by the firms lock them into the stage of
"rigidity," in which they will ultimately lose their competitiveness in the face of rapid
evolution of technology. The "dematuration" process will deter the usual process of
product standardization from capturing the advantage of the economies of scale. Rather,

it will proliferate the introduction of new products in the market and make the corporate

 

 

33

managers rethink their competitive strategies.

Another interesting empirical study by Dhalla and Yuspeh (1976) suggests that the
universal application of the PLC concept may be misleading. They found that, while it is
difficult to reverse the trend for the products in a final declining stage, no firm brand
behavior can be found with the first three stages of the PLC. The authors give a list of
examples that had been for a long time but were still full of vitality and whose sales went
up and down but stayed in the growth phase.

These anecdotal and empirical evidences suggest that the evolution of the product
life cycle is not given as conventional PLC theory. Instead, the product volume and mix
are truly the strategic variables whose behaviors are unpredictable. One interesting
inference from the literature is the difference in the width of product variety between the
growth stage of the reversed life cycle and that of the usual life cycle. The width of
product variety in the growth stage of the reversed life cycle will be much greater than that
of the usual life cycle. Hence, as the reversal of the product life cycle will generate more
uncertainty into the game of competition a firm must recognize the importance of
flexibility in enhancing its strategic competitiveness in the market. Potential reversals will
occur with substantial benefits to those who carefully prepare through decisions regarding

technology and management planning in a broad strategic fashion.

2.2.3 Integration of Product and Process Decisions

2.2.3.1 Product-Process Matrix

Integration of the product life cycle and the process life cycle has been best
described by "fit" (Hayes and Wheelwright 1984) or "alignment" (Pearson et a1 1991)
based on the market requirements imposed by a stage in the product life cycle and its
corresponding process capabilities to generate the optimal performance in meeting those

requirements (Pearson et a1 1991). Empirical studies (Abernathy 1978b; Hambrick et al.

 

 

34

1982a and b; Shipper 1981) have found that the production process life cycle begins with
a fluid, uncoordinated configuration and evolves first into a segmented and finally into a
specific, systematic configuration. This transition of the process life cycle corresponds to
the evolution from job shop, to batch, to assembly line, and finally to continuous flow
production. A conceptual framework to match properly the evolution of the process life
cycle by the evolution of the product life cycle is the product and process matrix (Hayes

and Wheelwright 1979a and b) as depicted in Figure 2.2.7

Process
Choice

Project 0
Job Shop 0

Batch 0

0
Continuous: 0
Process

ow Volume High Volume
Unique, Variety Standardized

 

 

Product Volume and Variety

Figure 2.2. Product and Process Matrix

Hill (1989) presented almost the same integration scheme with a different
approach, which is called "Product Profiling." He identifies important elements of both
marketing strategy and manufacturing capabilities and characterized the process choices
according to those elements. The process capabilities are matched with the market

requirements prescribed by the business chosen, mainly product range and volume. In this

 

7 For detailed reasonings for the match, refer to Hayes and Wheelwright [1989].

35

respect, this method is based on the conceptual framework of the product-process matrix.
However, he attempts to extend the product and process matrix by linking market
requirements and process capabilities through a concept called "order winning criteria,"
which is similar to the concept of competitive priority. He also asserts that the transition
described in the product-process matrix does not appear as a liner sequence (Hill 1989).
Firms may follow an iterative, repetitive course, jumping forwards and backwards
at different times according to their perceptions of market demands in the future and the
corresponding process design issues (Pearson et al 1991). For example, a firm will have a
job shop type of manufacturing process for one of its products. As volume increases a
transition to a batch type of process may occur. When volume decreases, towards the end
of the product life-cycle, the transition back to job-shop may take place (Hill 1989).
According to Hill's argument and those supporting the reversal of PLC, the
process choice may be very much dependent on the future market situation projected by
the firm. Based on past studies, it can be presumed that there may be two types of the
evolution of process technology. First, given the usual evolution of a product's life cycle
as being true and the theory of the product-process matrix as being accepted, the firm's
process technology will cycle with the transition from the flexible system, to the dedicated
and inflexible system, back to the flexible system, and so on. Second, if the product life
cycle stays in the growth stage by the reintroduction or design updates of the product
(Smith 1980; Ayres and Steger 1983), the firm may always focus its investment more on
the flexible manufacturing technology instead of blindly switching to the dedicated system.
According to the current trend of market conditions and technology development, the
former is not as persuasive as the latter. Thus, the investment in process technology
should be carefully planned according to the dynamic changes of the market requirements

in the firture.

 

36

2.2.3.2 Effect of Flexible Technology on Product-Process Matrix

The recent development of flexible technology allows firms to produce a wider
range of product variety and volume at the same or lower cost than does conventional
technology(Goldhar and Jelinek 1983; J elinek and Goldhar 1984; see also Suresh and
Meredith 1985). Hence, flexible automation has challenged the managerial wisdom of the
past decades that was captive to in the paradigm of product-process matrix theory (Adler
1988). It seems that the matrix's normative implication is no longer applicable to any

process choice with the higher level of flexible automation.

Process
Choice

Job Shop

Batch

Assembly
Line
Continuous K
Flow \ w

Unique Standardized
Low Volume High Volume

 

 

 

Figure 2.3. Effect of Flexible Automation on the Product-Process Matrix

In a dynamic perspective, it is necessary to consider the implications of recent
flexible automation, which make it possible to view the production of less standardized
products in a "quasi-continuous process" (Adler 1988). Hence, a new matrix's diagonal
would be flattened out, as in Figure 2.3 (Boothroyd 1982; see Hayes and Wheelwright
1984). In fact, Hayes and Wheelwright (1984) discussed the impact of flexible technology
as one of the limitations for the application of the matrix. They states that "such
improvements in production flexibility, without movement along the diagonal, might be

thought of as a third dimension to the matrix." Hence, if we separate this new dimension

 

 

37

from their product-process matrix, it will be possible to draw a new matrix as shown in
Figure 2.4. In this new matrix, the vertical axis represents the degree of flexibility of the
new process technology instead of the conventional category of process choice. Figure
2.4 depicts the wide range of variety and volume covered by flexible technology, whereas

rigid technology covers only standardized and high volume products.

 

 

 

Flexible
technology
Rigid
technology
Low volume High volume
Unique Standardized

Figure 2.4. Integration of Technology and PLC

One of the major implications of such a revision is the undermining of a widely felt
"intuition of a corollary between efficiency and rigidity” (Adler 1988). It can denounce
the proposition, as suggested in an empirical study by Abernathy (1978b), that there is a
fundamental dilemma between innovation and efficiency. The capability to produce a wide
variety of products at low cost is a major advantage of flexible technology. Hence, as
discussed in section 2.1.4, the most significant implication of the introduction of flexible
technology like FMS, CHM, and CAM is the coexistence of differentiation and low cost as

a firm's strategic focus.

2.2.4 Summary and Implications

According to the review of literature, the potential paths for the market/product

38

evolution may not be given as the main propositions of the PLC theory. As shown in
Figure 2.5, there may be four possible states of the market/product requirements a firm at
any stage of the PLC may encounter in the future. Each of these four possible states may
be reflected by a firm's overall competitive strategy as a combination between
differentiation and cost leadership (Porter 1980). Hence, in this dissertation, the four
states given in Figure 2.5 will be distinguished by two exogenous variables -- volume and
variety. Another finding by the literature review in this section is the possible coexistence
of differentiation and low cost strategies, which means the simultaneous pursuit of both
economies of scope and economies of settle by the adaptation of flexible process
technology.

In this dissertation, flexibility and cost efficiency are chosen as competitive
priorities representing differentiation strategy and cost leadership strategy, respectively.
Through the scenario analysis with a dynamic control model, the study will attempt to find
which strategy is suitable for each of the four possible states in Figure 2.5. The study will
also explore whether the coexistence of differentiation strategy and cost leadership

strategy is possible and under what condition it is possible.

 

 

Volume Volume
Increasing Decreasing
Variety
Increasing A 8
Variety
. C D
Decreasung

 

 

 

 

Figure 2.5. Four Stages of the Possible Evolution of the PLC

 

 

 

39

2.3 NEW PROCESS TECHNOLOGY AND
MANUFACTURING FLEXIBILITY

2.3.1 New Process Technology

2.3.1.1 Automation of New Process Technology

Automation is defined as "the technology concerned with the application of
complex mechanical, electronic, and computer—based systems in the operation and control
of production (Groover 1980). A very similar definition of automation can be found in the
Encyclopedia Britannica; " a significant substitution of mechanical, electrical, or
computerized action for human effort and intelligence a technology concerned with
carrying out a process by means of programmed commands capable of operating
without human intervention." Groover (1980) states that automation includes: (1)
automatic machine tools for processing work parts; (2) automatic materials handling
systems; (3) automatic assembly machines; (4) continuous-flow processes; (5) feedback
control systems; (6) computer process control systems; and (7) information systems to
support manufacturing activities. Groover also lists the economic and social reasons for
automating which include the following: (1) increased productivity; (2) high cost of labor;
(3) labor shortages; (4) trend of labor toward the service sector; (5) safety; (6) high cost
of raw materials; (7) improved product quality; (8) reduced manufacturing lead time; and

(9) reduction of in-process inventory.

2.3.1.2 Rigid Automation and Flexible Automation

Traditionally, the manufacturing system has been represented by two kinds of
equipment. The first one -- dedicated machinery, such as transfer lines -- is best suited for
mass production of a single part. This process specialization permits low unit costs, but it

inhibits flexibility. The second kind of equipment -- nonintegrated general purpose

   

 

40

machine tools -- is best suited for the very small batch production of different parts (Gupta
and Goyal 1989). Due to the high degree of flexibility, costs per unit tend to be high, but
the flexibility of the process can accommodate design changes, demand fluctuations, and
shifts in product mix.

These definitions -- dedicated and general purpose -- are applicable to the
concepts separating process choices used in Hayes and Wheelwright's product-process
matrix. Today, advanced manufacturing systems such as flexible manufacturing systems
(FMSS), computer aided design/manufacturing (CAD/CAM), and Just-in-Time (HT), offer
a new dimension for the definition of process technology -- flexibility (Groover 1980,
Gupta and Goyal 1989). With the combination of the concept of automation and flexible
system and dedicated system, automated process technology can be categorized into two
groups —- flexible automation and rigid automation (Groover 1980).

Whereas the rigid automation is related to cost efficiency, the flexible automation
is adopted to improve the firm's flexibility. Flexible automation like FMS and CIM has
brought about significant advantages over traditional batch manufacturing by reducing the
level of WIP inventory (Ranky 1983), manufacturing lead time, and improving utilization
of resources (J aikumar 1986). As compared to conventional systems, the greater
flexibility provided by such automation also allows it to be used for successive generations
of products and gives it a longer useful life than traditional process investments (Gupta
and Goyal 1989).

Cox (1981) refer to an experience of the Ford Motor Company to illustrate the
advantage of flexible automation over rigid automation; "to convert one plant to
manufacture six-cylinder engines instead of eight-cylinder engines, it was necessary to
remove and replace all the tooling in the plant It also suggests why, in an increasingly
uncertain and volatile worldwide market, a more flexible manufacturing technology would
offer economic advantages to mass producers." In addition, flexible technology also

provides many additional benefits such as better product quality, reduced inventory and

 

 

 

41

floor space, lower throughput and lead times, and faster response to market shifts
(Groover 1985; Hayes and Jaikumar 1988; Kaplan 1986). In their review paper for new
manufacturing technology, Naik and Chakravarty (1992) summarizes the characteristics of
the flexible systems with high fixed costs, low variable costs, dynamic reallocation and
coordination of resources, small lot sizes, and shift from the economies of scale to the

economies of scope.

2.3.1.3 Measurement for the Flexibility of New Process Technology

There are not many attempts to find the differences between rigid and flexible
automation. Groover (1980) separates these two automation with the fixed sequence of
the operations in the rigid automation versus the programmable sequence of the
operations in the flexible automation. This difference in programmability separates two
automation with the number of parts or products each automation can handle
economically. Groover lists the examples of two types of automation; (1) transfer lines,
automatic assembly, and so forth as rigid automation and (2) numerically controlled
machine tool as flexible automation.

The most frequently used measure for the flexibility of the production system in
past research (Burstein 1986; Groover 1980; Fine and Freund 1986) is the number of parts
or products produced. It is also consistent with the definition of production flexibility by
Browne (1984). In particular, Gaimon (1986) uses a flexibility index indicating a firm's
combined capability for product—mix, volume, quality of output, and customer service
acquired by the flexible technology. She considers the flexibility index as positively related
to the acquisition rate of the flexible technology acquired and to the growth of the firm’s
market demand.

These examples of both automation suggested by Groover (1980) and the
anecdotal evidence in the literature typify the measurement for the distinction between two

automation. The variety of parts or products that each automation can handle

 

42

economically provides the conceptual foundation for the measurement. In addition, there
is no clear border line between two automation. For example, we will call the automation
that can handle two products more rigid than the automation that can handle four
products. Hence, the relative distinction between two types of automation can be made
according to the degree of flexibility, defined as the number of products or parts
producible (see also Meredith and Suresh 1986).

Given the definition of a system's flexibility as being the number of producible
product (part) types the most critical issue in the measurement of a system's flexibility is
whether the system's flexibility is related to the system's capacity level in units of output.
It may not be appropriate to conjecture that the system's flexibility will increase as more
machines are installed, which has been a widely used assumption in the analytical studies
for new process technology. A good illustration is presented in the study by Nakarur
(1992). According to his measurement index for the system‘s flexibility, the system's
flexibility is independent on the number of machines in a system. Rather, the system's
flexibility is dependent on the flexibility of each machine, such as producibility of excess
capacity of other machines. Hence, the overall system's flexibility may have to be
considered independently of the capacity of process technology in the development of the

model in this dissertation.

2.3.2 Effects of New Process Technology

Trade-offs against the costs of the capital investment and the human resources are
a wide range of benefits attributed to new process technology. These benefits include
lower direct manufacturing costs resulting from reductions in setup time, processing time,
labor requirements, lead time, inventory, factory space, and so on (Jaikumar 1986). The
effects of new process technology can be grouped into two categories - internal effects
and external effects. Internal effects include mainly the effects on manufacturing lead time

and the cost structure of production system. External effects include the effect on the

 

 

 

43

firm's sales, usually the result of the internal effect.

2.3.2.1 Effects on Manufacturing Lead Time

Manufacturing lead time is the combination of processing time, setup time, and
waiting time. New process technology can reduce the significant portion of time a part or
product spends in the shOp (Jaikumar 1986) like throughput time, waiting time (Goldhar
and Jelinek 1983; Groover 1980), and setup time (Groover 1980). For example, one
major computer integrated facility at Messerschmitt-Bolkow-Blohm in Augsburg, West
Germany, reduced production lead time for the Tornado fighter plane from 30 months to
18 months(Jelinek and Goldhar 1983). A survey result by J aikumar (1986) also shows the
drastic cut in processing time to one third on average after the installation of FMSs.

One significant advantage of the lead time reduction is the reduction of WIP
inventory. Since the setup time becomes negligible in new process technology, the EOQ
can approach one as in JIT system. More importantly, the range of EOQ the system can
handle at reasonable costs may be much wider than the traditional system can handle.
Another important advantage is faster market response. The results of faster market
response include less safety stock, fast response to inaccurate forecast, and widened

product line (Meredith 1987).

2.3.2.2 Effects on Cost Structure

Total manufacturing costs include fixed cost and variable costs. Fixed cost is
related to capital investment in machinery and equipment in the factory. A survey result
shows initial investment cost in FMS ranges between $10 and $25 millions (Smith et al.
1986; see also J elinek and Goldhar 1984). The investment costs are higher from
traditional job-shop, automated transfer line, to flexible manufacturing system, like FMS
and CIM. Hence, each additional automated capacity increases the fixed cost of capital

investment. The flexibility embedded in the acquired new process technology may

 

 

44

increase fithher the fixed cost of capital investment. This proposition might be inferred
from the fact that the investment cost for the flexible production systems like FMS or CIM
is enormous compared to the cost for the stand alone NC machines.

Variable costs can be measured in terms of the order quantity and the variety of
products or parts processed. As discussed above, there are two types of variable costs --
the scale-related costs and the variety-related costs. Stalk (1988) suggests that, in a
flexible manufacturing system, the variety-related costs start lower and increase more
slowly as the product or part variety grows than in a conventional manufacturing system.
Hence, a flexible system enjoys more variety with lower total costs than do traditional
factories, which are still forced to make the trade-off between scale and variety.

Hough (1989) emphasizes the reduction in direct labor (see also Gaimon 1985; and
J aikumar 1986) and a probable flattening of the learning curve as the effects of
automation. He also points out the reduction in indirect labor and other elements of
overhead. Depending upon the degree of automation, he asserts, there should be a
diminished opportunity for workers to learn better ways of accomplishing a task, primarily
due to the reduction of direct labor. In addition, elements of learning due to
improvements in tool coordination, shop organization, and inventory systems would be
eliminated by the ability to simulate production processes and layouts prior to setting up a

plant (see also Levy 1960 and Wild and Port 1987).

2.3.2.3 Effects on Sales Growth

The effect on sales growth of new process technology is an indirect result of faster
market response and/or widened product line. As discussed above, new process
technology allows the firm to update products continuously and thus tends to increase the
range of product line, its complexity, and its rate of change. It also creates an entry
barrier against competition due to increased technological content of products, closer

market links, and improved responsiveness (Goldhar and Jelinek 1894). These overall

 

 

45

effects may increase a firm's market share. Roth et al. (1991) suggest that new process
technology improves the firms' market share through the anticipated improvements in
outputs and allows the firms to pursue a broader marketing strategy, thereby enabling
them to capture a portion of their competitors' demand.

The result of an empirical study by Thietart and Vivas (1984) shows that market
share increases as investment and assets increase for all kinds of industry involved in their
study. This result implies that the possible investment in new process technology has a
positive relationship with the sales growth of the firms. In summary, the acquisition of
automation of new process technology seems to be viewed usually as the substitution of
the new automated capacity for the manual output capacity to improve manufacturing
performance. Furthermore, the flexibility of new process technology is found to provide
the manufacturing firms with additional cost and benefits. Hence, there should be clear
distinction between the effects of the acquisition of mere "automation" of new process
technology and the effects of the acquisition of "flexibility" of new process technology.
This distinction should be considered in the process of the model development in this

study.

2.3.3 New Process Technology and Learning Effect

Learning effect is an widely discussed in many studies of developing or applying
the production fimction. Productivity improvement due to the learning effect was first
identified by Wright (193 6). Yelle (1979) found this effect existing in a number of
industries from early in this century. Levy (1965) develops a learning function based on
actual examples in which the rate of learning is related to the experience of workers which
are exogenous to the cumulative production volume. Levy claims that the productivity
improvement is realized by two factors, "the initial efficiency of the process" and "the
autonomous learning effect.” He assumes that "the initial efficiency of the process"

depends on the firm's preplanning efforts including testing prototype process, training or

 

46

education of workers, and so on. On the other hand, "the autonomous learning effect" is
assumed to depend on the cumulative experience of workers as the production system
runs. Hence, his learning fiinction implicitly assumes that the automated production
system with a low level of labor force may show a slow learning effect with high initial
efficiency compared to the conventional production system with a high level of labor
force. Hence, his learning fimction assumes "the flattening learning curve" as discussed in
section 2.3.2.2 (see also Hough 1989; Wild and Port 1987).

Recognizing the shortfall of conventional studies of the learning effect that dealt
only producing homogeneous products, Meredith and Cam (1989) introduce a new term
-- synergy -- to include the learning effect in a situation of adapting several new advanced
technologies to produce a variety of products. The authors present a unit production cost
fiinction incorporating this synergy effect. The fiinction assumes that the high synergy
effects among new advanced technologies adOpted by the manufacturing firm result in low
unit production cost. They contend this function can be used in determining the order of
acquisition of several new advanced technologies. The significance of their study is that
they provide an approach to finding the learning effect due to the synergy of new
advanced technologies. This is a significant development in view of the fact that the
conventional studies of the learning effect have focused only on the improvement in labor
productivity or labor cost. However, a major drawback of this study is that the synergy
effect can only be estimated after physical running of new advanced technologies which
would be almost prohibitive due to the huge amount of costs and time involved.

Roth et al. (1991) applied this synergy concept in developing their analytic model
to study the optimal acquisition of new flexible technology. They views the synergy effect
as causing a structural shift in the level of "technological progress" of the production
System, which they use as a new term for the learning effect reflecting gains in system
utilization and productivity. This learning effect is assumed to result from improvements

in layouts, machine loadings, machine speeds, yields, use and integration of system

 

47

components, and management methods. Like Levy's learning function, their model
implicitly assumes high system synergy afforded by new flexible technology results in a
flattening learning curve. This is the same as the view of others like Levy (1965), Hugh
(1989), and Wild and Port (1987) who argue that more automation leads to less room for

the learning effect.

2.3.4 Analytic Studies for New Technology Acquisition

Analytic studies in new technology acquisition have many different approaches in
terms of methodology. Most studies have used either mathematical programming
(Burstein 1986; Fine and Freund 1986; Li and Tirupati 1992) or optimal control theory
(Amit and Ilan 1990; Gaimon 1985, 1986, 1989, 1992; Roth et al. 1991 . Addressed in
these studies are the issues such as trade-offs between scale and scope, the timing of
acquisition, the size of acquisition, and the effect of learning on optimal acquisition. In
this section, several key models are presented and discussed with issues related to this

dissertation.

2.3.4.1 The Model of Fine and Freund

Fine and Freund (1990) presents a two-stage stochastic model for optimally
choosing a portfolio of flexible and nonflexible manufacturing capacity. Their model is
very similar to Burstein's (1986) except their model includes stochastic demand and
multiple products. A similar study by Li and Tirupati (1992) presents a nonlinear model
for the technology choice with concave cost functions. The focus of Li and Tirupati's
study is the development of an efficient heuristic algorithm. Fine and F reund's model
focuses on the economic tradeoffs between the acquisition costs of flexible capacity and a
firm's ability to respond flexibly to future uncertain demand. They claim that the model
captures a technology's flexibility characteristics with greater revenue resulting from

broader product mix capabilities and lower operating costs due to economies of scope.

 

48

Fine an Freund formulate a single-period, multi—product, dual-technology problem
to maximize the total profit by netting the technology acquisition costs and the

manufacturing costs out of the total revenue. Below is the formulation of their model.

1

max —rFKF —§jer-KJ- +Ziipi§{Rij-(Yrj +Zij)—Cj '(Yij +25»,

{KF,KJ-;j=l ..... n}
{Yij-,Zij;i=l,...,k,j=l ..... n}
subjectto Yij—K,- :0, i=1 ..... k j=l ..... n,
Zle—KFSO’ 1:1 ..... k,
J
Yij-20, i=1 ..... k, “l ..... n
Z620, 1:1 ..... k J—1,... n
K120, j—l ..... n
KF20

The first two terms in the objective function are the technology acquisition costs
for both flexible and dedicated technology. The term, pi, represents the probability
associated with the state i of the world for market demand. The term, Rij(Yij+Zij)» is the
revenue function of product family j in state i. In their sensitivity analysis, the authors
assume the revenue functions to be quadratic functions (i.e., linear demand functions).
The term, CJ-(Yij+Zij), represents the manufacturing variable cost. The terms, Yij and Zij,
represent the amounts of product family j in state i produced by dedicated capacity j and
flexible capacity, respectively.

In numerical examples, due to the enormous number of possible states of the world
and the condition for uniqueness of the optimal solution, Fine and Freund carry out
sensitivity analysis in a two-product case. Moreover, by assuming that the variable costs
for either technology are very low, they exclude the variable costs from the objective
fiinction. In the first sensitivity analysis, they explore analytically the sensitivity of the
optimal capacity levels to the acquisition costs. In the second sensitivity analysis, they
explore the sensitivity of the optimal capacity levels to the changes in the distribution of
demand. A major drawback of their model is that it separates two technologies only by

their acquisition costs, and thus it misses many potential benefits of flexible technology in

 

49

the formulation. In fact, the elimination of the economies of scope from the model, i.e.,

the elimination of variable cost, may diminish the significance of their study.

2.3.4.2 The Model of Gaimon

Gaimon conducted many studies related to automation (June 1985, September
1985, 1986, 1989, 1992). Her studies have addressed many issues such as diminishing
returns (1985a), productivity enhancement of labor (1985b), application of game theory
(1989), and lagged learning effect (1992). In most of her work, she views the acquisition
of automation as the substitution of automated capacity for manual output capacity.

Below is one of her models that is closely related to this dissertation.

j: {[p(t) - BU) — b(1)]d(t)— c(t)a(t) + s(t)r(t) — m(t)q(t)}c'“d[
+gfme‘” + mime-rT

subject to

d(t) = d1(t) - d2(t) + d3f(t),

q'(t) a(t) - r(t), (1(0) =qo,

b'(t) = -0t(t)a(t)b(t), b(0) =bo,

f(0 = 13(080), f(0)=fo,

d(t) 2 0, t e (0,T),

d(t) SQG), t6 (0,T),

c1(0 - a(t) 2 r(t),

a(t) e (0, A(t)), r(t) e (0,R(t)), p(t) 2 0.

In this model, the term, f(t), represents the flexibility index summarizing a firm's
capabilities with respect to product-mix, volume, quality of output, and customer service.
Therefore, this model assumes, as described in the demand function, that a firm with a
high index level of flexibility has the potential to increase its demand by manufacturing
products whereas a firm with less flexible productive capacity may be unable to produce.

One important aspect of this model is related to the state equation for the flexibility index.

 

50

The term, B(t), is defined as a measure of the effectiveness that acquiring
technology has on increasing the level of flexibility index at time t. Gaimon defined this
term as a function of time to capture the possibility of anticipated technological
advancement. According to the constraint for f(t), the more flexible the system, the
higher the value of B(t), and Vice versa. In addition, according to the third and fourth
constraints, both the unit operating cost and the system's flexibility depend on the
acquisition rate of automation. As discussed in Section 2.3.2.3, flexibility is the capability
of the system which may be independent of the level of automation capacity. Therefore, it
might be appropriate to have a control variable representing the rate of acquired flexibility
independent from both the rate and the accumulated level of acquired automation

capacity.

2.3.4.3 The Model ofRoth et al.

The model of Roth et al. (1991) is an extension of Gaimon's model (1986). Roth
et al. consider in their model the lagged learning effect, called as "technological progress,"
the market share increases due to the acquisition of new process technology, and the
reduction of unit operating costs. In this model, the flexibility of the process system is
proportionate to the rate of acquired flexible automation capacity. Thus the impact on
market share and unit operating costs is proportionate to the rate of acquired flexible

automation capacity. Below is the formulation of their problem.

Max [o,s(r) +G,k(T) +G3a(T)]E(T) —joT{n[s-s12 +c1a2+c3r2
+[B+c3]s+c,[dk -s]2 -c,[dk -s]}Edt

subject to

s' = yl(a+ak)(N-s)+yzs.
x' = a.

k' = a + onk - 1'.

OL' = -\llOL(1-¢8/X).

51

d3 = -BaC3.
a e (0, A), r e (0, R).

They assume that all demand is met through the available operating capacity or
through the use of short-term measures that increase capacity. They also assume
incremental timing strategy to upgrade a firm's capacity by asserting that radical diffusions
of new technologies may cause disruptions and be more costly than are incremental
diffusions. Hence, the cost functions for acquiring flexible automation and retiring
conventional capacity are defined as quadratic. The presence of the capacity variable, k,
makes the model capture the impact of technological progress due to the organizational
learning in the total capacity and the market share. Hence, the change in total capacity
and the market share over time is the function of both the change in flexible automation
capacity and the change in the total capacity due to technological progress.

Interestingly, this technological progress factor, or, is considered as a state variable
that is decreasing over time. Roth et al. also assume the rate of decrease in technological
progress over time is decelerated by the system's synergy effect (Meredith and Cam
1989). This concept is the same as the flattening of the learning curve due to the
acquisition of automation as discussed before. However, empirical evidence suggests that
the learning effect is a fiinction of cumulative experience, which is usually represented as a
cumulative production level (see Yelle 1979). Hence, it may be appropriate to relate the
value of reduction factor, w, to the cumulative demand level rather than to choose an
arbitrary value for the factor. Overall, the contribution of this model is the extension of
previous studies in this area including the detailed modeling aspects with the consideration
of the interaction effects of both the organizational learning and the acquisition of flexible

automation on both market share and unit operating costs.

52

2.3.5 Summary and Implications

In the review of the past analytic studies, there are three findings: (1) Lack of
consideration for the flexibility aspect of new process technology; (2) Failure to link
technology acquisition decisions with the corporate strategy; (3) Single dimensional
definition of process technology as production capacity in units of output. In fact, these
drawbacks are found mainly due to the single dimensional definition of new process
technology as production capacity in units of volume. It is expected to enhance the
richness of analytic models in the future studies with continuous efforts in developing
conceptual works to distinguish two aspects of new process technology, automation
capacity and flexibility. Hence, this dissertation attempts to propose an analytic model
overcoming the drawbacks of the past studies by considering the two-dimensional aspect

of new process technology.

2.4 RESEARCH STATEMENT

This dissertation addresses the linkage of the strategic orientation of SBUs
regarding cost efficiency and flexibility with the acquisition decisions for new process
technologies. Specifically, this study attempts to provide managers with a strategic
framework for the acquisition decision process of new advanced technology as a practical
tool for strategic decision making in technology acquisition. The focus of this study is on
the development of an optimal control model for optimal decisions regarding the
acquisition of new process technology to enhance the production volume and variety
capability of the system and, using the scenario analysis, on the investigation of
relationships between process technology choice and competitve priorities (cost efficiency
vs. flexibility) of a firm's SBU.

The contribution of this study is also due to the fact that the acquisition of new

process technology is considered to affect the production system along two important

 

 

53

dimensions, its production capacity in units of output and its ability to produce a variety of
products or part-types. The choice of the mix of these two dimensions of process
technology is also considered to affect its market potential regarding volume and variety in
addition to unit production costs. The objective function considers the trade-off between
the market potential and SBU goals regarding volume and variety. Through systematic
scenario analysis, the evolution of process technology choice in relation to the strategic
competitive priorities of the SBU along the stages of the product life cycle is thoroughly

investigated.

CHAPTER HI

STRUCTURAL FRAMEWORK FOR THE STRATEGIC
ACQUISITION OF NEW PROCESS TECHNOLOGY:
A CONCEPTUAL FRAMEWORK

As discussed in the previous chapter, the acquisition of new technology has
significant strategic implications for a firm's long term market competitiveness. The
understanding of relationships among various contingent variables surrounding a firm's
internal and external operation environment is a prerequisite for the analysis of the
strategic implications of technology acquisition (Madu and Georgantzas 1991). The
structural framework proposed in the first section explains how technology decisions are
related to the corporate strategy through the business strategy. This relationship will be a
conceptual basis for developing the objective function of the model presented in Chapter
4. The following two sections explain how changes in a firm's market demand and
manufacturing cost structure are affected by other variables. Hence, these two sections
provide conceptual bases for the development of constraints of the model in Chapter 4.

The last section introduces a new concept called the "Product-Technology Box"
by incorporating two dimensions of technology -'- automation and flexibility -- into the
normative concept of product-process matrix. This new concept has been supported by
many researchers (Boothroyd 1982; Hayes and Wheelwright 1984; Adler 1988). With
the results from the scenario analyses at the end, this research attempts to explore how the
best fit between technology choice and market requirements within the new "Product-
Technology Matrix" evolves due to the optimal acquisition of new process technology

along the stages of a product life cycle.

54

55

3.1 DECISION PROCESS FOR NEW TECHNOLOGY ACQUISITION

The decision process for the acquisition of technology is comprised of three levels
of decision as depicted in Figure 3.1. In this structural diagram decisions for the
acquisition of new technology made at the functional level are linked to corporate strategy
and business strategy. Decisions related to the corporate strategy are the relative
importance weight given to a particular strategic priority, either flexibility focus or cost
efliciency focus, depending upon whether the firm pursues a differentiation strategy or a
cost leadership strategy (Porter 1980). Decisions related to business strategy include
each SB U 's goals for the growth of market share in volume and the growth of market
share in product @art) variety.

The growth of market share in volume is related to the corporate strategy of
pursuing cost efficiency whereas the growth of market share in product (part) variety is
related to the corporate strategy of pursuing flexibility. Firms with the strategic priority of
cost efficiency will try to achieve economies of scale by increasing sales in volume. On the
contrary, firms with the strategic priority of flexibility will try to achieve economies of
scope by increasing sales in variety. Hence, the goals of a firm's SBU are closely related
to the strategic priority stipulated by the corporate strategy. In addition, the real values of
SBU's goals are to be set according to the firm's projection for future market conditions
along the market product life cycle.

When the SBU's goals are set at the business level, decisions regarding the
acquisition of new technology will be made at the functional level. Decisions for the
acquisition of technology include the timing and size of investment in automation
capacity of new process technology, flexibility of new process technology, and the
reduction of conventional capacity. Since new process technology is defined in two
dimensions - automation capacity and flexibility - in this dissertation, the acquisition of

new process technology entails two separate decisions, one for the acquisition of

56

automation capacity and another for the acquisition of flexibility.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Corporate
Strategy
Strategic
Priority
Business
Projection of Strategy
Future Market
Condition SBU's
Goal
Funcflonal
Actual Strategy
Market
Position Technology
Decision
Internal
______> Production

Environment

 

 

 

 

Performance

 

 

- Flexibility
- Cost Efficiency

- Sales Growth in Volume
- Sales Growth in
Product (Part) Variety

- Purchase of
Automation Capacity

- Purchase of Flexibility

- Reduction of
Conventional
Capachy

- Product Volume
- Product Variety
- Production Cost

- Flexibility

- Total Manufacturing
Cost

- Strategic “Fit”

Figure 3.1 Structural Diagram of the Decision Process for
the Acquisition of New Advanced Technology

As discussed in Chapter H, many have asserted that optimal decisions for the

acquisition of technology should be contingent upon the strategic priority decided at the

corporate level and the following SBU's goals decided at the business level (Porter 1980;

57

Skinner 1967; Simmonds 1981; see also Swamidass and Newell 19878). Hence, the model
in this dissertation includes variables related to the SBU's goals and the coefficients related
to the strategic priority. In doing so, this dissertation explores how decisions for the
acquisition of new technology are related to the desired business goals as well as the firm's
strategic priority.

According to Figure 3.1, decisions for the acquisition of new technology affect
internal production environment, which consists of production volume, production variety,
and manufacturing cost structure. Technology decisions also affect the firm's actual
market position in terms of both sales volume and product variety (refer to section
2.3.2.3). Hence, the model in this dissertation will measure both product volume
flexibility and product mix flexibility of the firm by comparing the internal production
environment with the actual market position. Detailed relationships among variables
related to technology decisions will be discussed in the next section.

The overall performance to be measured in the model includes not only the total
costs related to both technology investment and production as well as flexibility in terms
of production volume and product variety, but also the costs related to the strategic "fit,"
which represents the consistency of decisions made at various levels in the hierarchy of
strategy as emphasized explicitly or implicitly by many researchers (F rohman 1982, 1985;
Garrett 1986; Hayes and Wheelwright 1984; Schroeder 1990; Skinner 1974; Wheelwright
1984). The relationship between conceptual linkage from corporate strategy to
production environment and actual scheme for measuring the overall performance in the
model is depicted in Figure 3.2, in which the corporate strategy dictates SBU's goal, the
technology decisions affects SBU's actual demand, and the SBU's production environment

dictates its production decisions. The model can measure the strategic "fit" by comparing

 

8 This empirical study reveals the positive relationship between the perceived environmental
uncertainty and the manufacturing flexibility of a firm. In this study. the manufacturing flexibility is
measured with respect to the product mix flexibility.

58

SBU's goals with its actual demand while it can measure flexibility by comparing actual

demand with production decisions.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Business Goal Actual Demand Production
A _ _ Decisions
Volume ‘ Volume Volume
Variety Variety Variety
11 ii 1
Corporate Strategy Technology Acquisition Production
A __ A Environemnt
Cost Efficiency ‘ Automation Capacity T Volume
Flexibility Flexibility Variety

 

 

 

 

 

 

 

 

 

Figure 3.2 Linkage Among Various Decisions

Table 3.1

Objectives of The Acquisition of New Technology

 

Tacficaland

Strategic Objective , , _
Operational Objectives

 

Consistency with the Flexibility maximization
firm’s competitive - volume
strategy - variety

Min [ Desired Business Total costs minimization
Goals -Actual Market - investment cost
Position ] - production cost

 

 

 

 

As described in Table 3.1, the strategic objective for technology decision making,
called strategic fit, is the minimization of the deviation between a firm's actual market
position through the acquisition of new technology and the desired business goals to align
the acquisition of new technology with the finn's overall competitive strategy. The tactical

objective in this model is the minimization of costs related to the product volume flexibility

59

and product mix flexibility. Operationally, the acquisition of new technology should be
decided to minimize the total costs combining fixed investment costs and the production
variable costs through trade-offs with flexibility (Simmonds 1981). Particularly, by
incorporating the strategic objective, the model can measure linkage between the strategic
priority of corporate strategy and the acquisition decision for new advanced technology of
functional strategy. Hence, the optimal control model presented in Chapter IV attempts to
capture the linkage among strategies in the hierarchy of strategy and measure this linkage

as the overall performance.

3.2 TECHNOLOGY AND EXTERNAL EN VIRONIVIENT

There are two possible causes for changes in a firm's aggregate demand. One of
these causes is the change in internal competitive force, and the other is the change in
external competitive force. This type of view can be found in the well-known Vidale-
Wolfe sales response model (1957). Internal change may include change in delivery
performance, service quality, product quality, or information systems for data
communication. External change may include change in the position of the product in its
own product life cycle, elasticity of demand due to competitive forces, general economic
conditions, or other environmental forces (Roth et al. 1991).

As depicted in Figure 3.1, the actual market position of a manufacturing firm is
assumed to be postulated in terms of both volume and variety. Anecdotal and empirical
evidence provided in the literature suggest that the acquisition of new process technology
improves a firm's market position (Goldhar and Jelinek 1984; Thietart and Vivas 1984).
Unfortunately, these studies fail to address the distinction between improvement in market
position in terms of volume and in terms of variety. Despite the lack of support from the
literature, this study proposes that the improvement of market position in terms of volume

is more closely related to the level of investment in the automation capacity of new

60

technology, whereas improvement of the market position in terms of variety is more
closely related to the level of investment in the flexibility of new technology. The
reasoning of this proposition is that volume change in market demand can be better met as
more investment in new technology capacity improves the capacity utilization of a
production system to meet the variation of sales in volume (Roth et al. 1991), whereas
variety change in market demand can be better met as the acquisition of flexibility
firrnishes a production system with more flexibility since the customer's brand loyalty may
be enhanced due to the benefits of better product quality, reduced lead time, and fast
response to market shifts (Groover 1985; Hayes and Jaikumar 1988; Kaplan 1986).
As discussed in the previous section, minimization of the discrepancy between the
SBU's goals and its actual market position plays a pivotal role in linking the strategic
Priority of the corporate strategy and decisions for the acquisition of new technology of
the functional strategy. Since the technology decision affects a firm's actual market
Position, and the business goal is a replica of the corporate strategy with respect to sales
gr OW‘th and introduction of new products, by matching these two in close proximity,
managers can make the technology decision that is sound and consistent with the

Objectives stipulated by corporate strategy.

3.3 TECHNOLOGY AND MANUFACTURING COST STRUCTURE

In this study it is assumed that a firm pays a fixed investment cost for new
techlIOlogy capacity and its flexibility separately. In fact, a stand-alone NC machine
usually costs around $300,000 to $400,00 whereas an FMS, which combines these NC
macllines with other automated systems to increase the flexibility of a production system,
can CO St sometimes tens of millions of dollars (Bobrowski and Mabert 1988). Hence, the
fleXibility embedded in new process technology will require more investment in addition to

the . . . . .
b21810 cost for mere automation capacrty as shown in Figure 3.3.

‘

 

61

Fixed

investment (1

Cost /
Flexible
Automation

$100,000 /
/ Rigid
/ Automation
$0

0 . 100,000 uints
Capacrty

 

Figure 3.3 Technology and Investment Cost

As Stalk (1988) suggests, this study assumes there are two types of production
variable costs — volume—related variable costs and variety-related variable costs. The
average volume-related cost decreases as the degree of automation increases (see Roth et
al. 1 99 1). This type of cost decline results from the elimination of manual activities in
Production capacity due to the acquisition of new process technology. It is important to
note that as the cumulative automation capacity increases, there will be less opportunity to
gain the learning effect due to the low level of labor—intense activities and the high level of
integration among machinery and tools (Wild and Port 1987). Hence, the rate of decline
Of VOIUme-related variable costs will decrease as the degree of automation increases (see
Figul‘e 3.4). In the model the initial value of volume-related variable costs of a production
Syst em will be determined by its initial degree of automation. In this dissertation, the
VOlurTle-related learning effect is not considered.

The average variety-related costs reflect the costs of complexity in manufacturing,
including setup, material handling, inventory, and many of the overhead costs attributed to
the degree of product or part variety of a factory (Stalk 1988). The average variety-
related costs increase as the production system processes more product (part) types.

Si . . . . . . . . .
flee the system's flexrbrlrty 1n thrs dissertatron rs defined as the number of unique product

‘

62

(part) types to be produced, the average variety-related costs increase as the system's
flexibility increases as depicted in Figure 3.5. It is also assumed that the rate of increase in
average variety-related costs becomes smaller as the cumulative system's flexibility

increases.

Volume-
Related
Variable
Costs

 

$0

 

Low High
Degree of Automation

Figure 3.4. Automation and Average Volume-Related Variable Costs

Variety—
Related

Variable
Costs

 

$0

 

Low High
System's Flexibility

Figure 3.5. Flexibility and Average Variety-Related Variable Costs

3.2.1 PRODUCT-TECHNOLOGY BOX

In this dissertation new process technology is defined in two dimensions --

¥

 

63

automation capacity and flexibility. Definitions for these two dimensions were already
discussed in Chapter 11 (see section 2.3.1). Automation capacity of new technology is
defined as production capacity substituted for the conventional production capacity.
Flexibility is defined as the capability of the production system in terms of the number of
product or part types the system can produce. Hence, any process technology can be
defined by the combination of the degree of automation and the degree of flexibility. This
definition of process technology is quite different from the definition of process
technology found in the product-process matrix prOposed by Hayes and Wheelwright
( 1 97 9a).

In the product-process matrix the process technology is defined by the
combination of a fixed degree of automation and a fixed degree of flexibility, such as high
automation/low flexibility for transfer line and low automation/high flexibility for job shop.
Unfortunately, as Hayes and Wheelwight (1979b) pointed out, their product-process
matrix seems to be inappropriate as new process technology with a high degree of both
automation and flexibility becomes available. New process technology allows firms to
PTOduce a wider range of product variety and volume at the same or lower cost than does
conventional technology (high-automation/low-flexibility or low-automation/high-
flexibility) defined along the vertical axis of the matrix. Hence, new process technology
might allow firms to pursue both cost efficiency and flexibility as the same time (Porter
1980) . Therefore, to be a viable paradigm of manufacturing strategy the product-process
matrix may need to be revised to fill the gap between the existing normative concept

behind the product-process matrix theory and the current trend of rapid diffusion of new

proceSS technology.

 

 

64

Flexibility

High

 

Low

 

High

Automation

Figure 3.6 Technology Matrix

To fill such a gap, a new term, called a "Product-Technology Box," is proposed to
integrate the PLC and a firm's technology choice as depicted in Figure 3.6. Unlike the
product—process matrix suggested by Hayes and Wheelwright (1984), a new matrix, called
a "Technology Matrix," combining automation in the horizontal axis and flexibility in the
vertical axis is first devised to represent technology choices (see Figure 3.6). Technology
choices on the diagonal in the new technology matrix have the same definitions as in the
pr Oduct-process matrix. The upper left corner (Job Shop), labeled as point A in Figure
3.6, represents a technology choice with high flexibility and low automation. The lower
right corner (Transfer line), labeled as point E in Figure 3.6 , represents a technology
ChOiCe with low flexibility and high automation. The major difference of this new matrix is
that any technology choice can be defined with the combination of the level of flexibility
given by a point on the vertical axis and the level of automation given by a point on the
horizOntal axis.

Any off-diagonal points, such as B and D, have different definitions than in the
Conventional product-process matrix. In the conventional product-process matrix, the
firm at point B has the same process technology as the firm at point A. In this new matrix
the firth at point B (Firm B) has higher level of automation than the firm at point A (Firm

A) and the higher level of flexibility than the firm at point C (Firm C). The firm at point D

‘

65

(Firm D) has a lower level of flexibility than Firm A and a lower level of automation than
Firm C. Hence, it is certain that Firm B (Firm D) needs more (less) fixed capital
investment for its process technology than does Firm A or Firm C.
According to the normative concept of the product—process matrix, Firm A is
suitable for the product structure of low volume and high variety whereas Firm C is
suitable for the product structure of medium volume and medium variety. In this new
matrix, Firm B has not only a high degree of flexibility, as does Firm A, to operate
efficiently for up to high levels of product variety but also the medium degree of
automation, as does Firm C, to operate economically for up to medium size of product
volume. Therefore, Firm B might be able to achieve the economies of scale of Firm C as
well as the econorrries of scope of Firm A, simultaneously. On the contrary, Firm D has a
technology choice with the same level of flexibility as Firm C and the same level of
automation as Firm A. According to the normative concept, Firm D would be suitable for
the pro duct structure of low volume and medium variety. Unfortunately, this kind of
produ ct structure cannot be found along the stages in the normal product life cycle.
consequently, the technology choice of Firm D is not considered in the conventional
Product-process matrix. However, since this choice has a lower level of fixed capital
investrnent than others such as A, B, and C, and a firm's technology choice changes
ConStantly over time due to the acquisition of new technology, this choice as initial process
tecl'11‘1010gy might have an advantage over the other positions in the dynamic environment.

Therefore, this study does not exclude this position in the scenario analysis.

To this new technology matrix, a third dimension is attached to represent the
Stages of product life cycle. This third dimension is the same as the horizontal axis of the
Conventional product-process matrix. The product structures of the introductory stage
through the declining stage of product life cycle, from low volume/high variety to high
volume/low variety, are represented along this additional dimension. Hence, the "Product-

e(“'hl'lology Box" combines all three drmensrons in one diagram as depicted in Figure 3.7.

‘

 

66

A point in the box has a corresponding product structure of a stage of the PLC and a
corresponding technology choice. For example, at point X in Figure 3.7 Firm F is
operating with a technology choice of a high degree of both automation and flexibility and

facing with a product structure of the mature stage of the PLC.

 

 

 

-<—i"'r—“rn">rmr-n

 

 

 

AUTOMATION

 

 

 

Figure 3.7 Product—Technology Box

One important feature ofthis box is that it is possible to identify the range of
pr Oduct structures suitable for any technology choice defined by the new technology
matrix) which would be difficult with a two—dimensional matrix such as the product-
p r 0(3683 matrix. For example, Firm B might be suitable for the product structures ranging
from the introductory stage to the growth stage of the product life cycle. A firm
positiOned at the upper right corner ofthe technology matrix may be suitable for the
Product structures ranging from the introductory stage to the mature stage ofthe product
life c=ycre.

With the above new concept of Product-Technology Box, this dissertation

a . .
ttempts to explore analytically how a firm's technology chorce evolves on the new

‘

 

67

technology matrix according to the transition of PLC and the firm's choice of competitive
priority for each stage of PLC. In particular, this study tries to identify how the evolution
of technology choice on the technology matrix differs according to a firm's competitive
priority -- either flexibility or cost minimization. This study also tries to explore how the
technology choice evolves in two different market environments of the normal PLC and
the reversal of PLC. Significantly, the analysis of the interaction effect between the types
of PLC and the competitive priority will provide managers with an insightful guide for
how to make optimal acquisition decisions for new process technology consistent with the

competitive strategy under different projections for future market behavior.

CHAPTER IV
THE MODEL

In this chapter, a dynamic optimal control model for the acquisition of new

technology is developed according to the conceptual frameworks proposed in Chapter III.

This chapter is separated into three parts. In the first part, the general description of the

model is provided along with the assumptions and the variable definitions. The second
part presents the objective function of the model. The state constraints and the control

variable boundaries are depicted in the third part. The model for this study follows the

standard approaches taken by many analytic studies in the literature on process

technology. The notation for time, (t), is suppressed in the objective function and the

subsequent constraints unless it is required.

4.1 INTRODUCTION

This section starts with a brief description of the proposed model. Then the

assumptions for the model are presented, followed by the description of the notation used

in the model.

4°1‘ 1 General Description of The Model
As mentioned in chapter 11, few past analytic studies regarding process technolog

rWestrnent have consrderedflexzbzlity as one of the objectives in their models. Past

a . . . . . .
nalYtlc studies related to process technology generally vrew the acqursrtron of technology

a . . . . . .
S the capacrty expansron, frequently combined With the learning effect. The major

68'

‘

69

drawback of their models is that they consider the cost efficiency as the only objective.
The model presented here includes not only cost efficiency but also costs related to
flexibility as the performance measures in the objective fiinction. Particularly, by
incorporating implicitly relative importance weights placed on two strategic priorities, cost
e ficiency and flexibility, according to the corporate strategy, the model attempts to
embed the linkage between the corporate strategy and the functional strategy. Hence, it
allows decision makers to render the optimal policy not only for its best positioning of
technology choice in the product-technology matrix but also for the technology investment
strategically viable according to the relative emphasis given to each of two competitive
priorities -- cost efficiency and flexibility.

The model in this dissertation considers both product volume flexibility and
product mix flexibility. These two types of flexibility are included explicitly in the
Objective function as the difference between actual production in volume and market
demand in volume and the difference between actual production in variety and market
demand in variety. Hence, any deviations in terms of volume and variety incur costs
deteriorating the overall performance of a firm's SBU. According to the perspective of
this study, the types of flexibility considered in the model should adequately portray the
strategic implications of new technology acquisition conditioned by the external strategic
variables, actual market demand in volume and variety. In this regard, the above two
types of flexibility are considered as the most suitable ones in reflecting strategic
implications with respect to the positioning of technology choice in the product—

technology matrix proposed in Chapter III (see Figure 3.6)-

4'1 '2 Assumptions

1‘ The firm is manufacturing products of a single type.

2‘ The market behaves in perfect competition. Hence, there is no dominant

competitor in the market.

70

3, There is no change in the technology choices of the competitors in the market.
4, The prices are fixed over the planning horizon.
5, New advanced technologies are readily available and there is no barrier for

individual firms to access new advanced technologies.

6. Changes in the aggregate market demand follow a smooth transition as in the PLC
curve.
7, There is no volume related learning effect in the average volume-related costs with

new process technology.

8. The average variety-related costs are decided by the flexibility of the system, not
by actual product (part) variety produced by the system.

 

8. There is no change in managerial control policy, other than specified by the
variables in this model, over the planning horizon.

9. The timing policy for the process technology acquisition is smooth, evolutionary
changeover from old to new technology within an existing plant.

4.1.3 Variables
In this section, endogenous variables and exogenous variables are grouped
separately. State and control variables are defined as endogenous variables. Cost

CoetTicients and other parameters are defined as exogenous variables.

4.1.3. 1 Endogenous Variables

X“) = accumulated level of automation capacity of new technology as production
capacity in units of output at time t, x(0) = x0, (state variable),

k“) = total production capacity in units of output at time t, k(0) = k0, (state variable),
SQ) = overall system flexibility (manufacturing flexibility) defined as the number of
products (parts) the production capacity handles at time t, 5(0) = so, (state
variable),
V“) = aggregate market demand of the firm in units of output at time t, v(0)=v0, (state

y ._

71

variable),

m(t) = market demand of the firm in number of products (or parts) types at time t, m(0)
= m0, (state variable),

b1(t) = average volume-related variable cost at time t, b1(0) = blo, (state variable),
b2(t) = average variety-related variable cost at time t, b2(0) = b20, (state variable),

a(t) = acquisition rate of automation capacity of new technology in units of output at
unit time t, a(t)e(0,A(t)), (control variable),

f(t) = acquisition rate of flexibility of new technology measured in number of product
(part) types newly acquired new technology can handle at unit time t, f(t)e
(0,F(t)), (control variable),

r(t) = rate of scrapping/reducing the level of conventional (existing) capacity in units of
output at unit time t, r(t)e(0,R(t)), (control variable),

p(t) = rate of actual production in units of output at unit time t, p(t)e(0,P(t)), (control
variable),
q(t) = rate of actual production in number of product (part) types at unit time t, q(t)e

(0,Q(t)), O < Q(t) S k(t)/\i/, (control variable).

4.1.3.2 Exogenous Variables
Yv(t) = total market size exogenously determined in terms of aggregate volume at time t,

Ym(t) = total market size exogenously determined in terms of product (part) variety at
time t,

V(t) = business unit (SBU) goal level of aggregate demand in units of output at time t

M(t) = business unit (SBU) goal level of demand in number of product (part) types at

time t,
c1 = acquisition cost per squared unit of automation, a(t),
02 = acquisition cost per squared unit of flexibility, f(t),
c3 = scrapping/reducing cost per squared unit of conventional (existing) capacity, r(t),

c4 = cost per unit squared deviation between the actual production in volume and the

 

0
UI

e1

e2

7:1

7‘2

(hr

(1)2

Br

B

91

92

72
market demand in volume,
= cost per unit squared deviation between the actual number of products (parts)
types produced by the production capacity and the number of products (parts)
types demanded by the market,

= cost per unit squared deviation between actual aggregate market demand in
volume and SBU's goal in volume,

= cost per unit squared deviation between actual number of products (parts) types
demanded by the market and SBU's goal in number of products (parts) types,

= market response factor per unit of acquired new technology capacity for the
change of SBU's aggregate demand in volume, 0 < n1 < 1/A(t),

= exogenously predetermined coefficient representing natural growth/decay of the
product life cycle in volume, -1 < n2 < DV, where DV is a predetermined upper

bound (saturation level) on the growth factor in terms of volume,

= market response factor per unit of acquired flexibility for the change of SBU's
demand in variety, 0 < (1)1 < 1/F(t),

= exogenously predetermined coefficient representing natural growth/decay of the
product life cycle in variety, -1 < (1)2 < Dm, where Dm is a predetermined upper

bound (saturation level) on the growth factor in terms of product (part) variety,

= effectiveness factor of acquired automation capacity on reducing average
volume-related cost, 0 < or < 1,

= the lower bound of b1, the volume-related cost,

= effectiveness factor of acquired flexibility on reducing average variety-related
cost, 0 < [32 <1,

= effectiveness (synergy) factor of acquired flexibility of new technology on
improving overall system flexibility due to the system synergy, 0< p1 < 1,

= effectiveness factor of reducing system's overall flexibility due to the attrition of
conventional (existing) capacity, 0< p1 < 1,

= a large number in the constraint for the relationship between a and f,

= a large number in the constraint for the relationship between p and q,

73

w = a coefficient representing the proportionate loss of volume capacity due to the
production of one product (part) type,

r = a coefficient representing the proportionate loss of system's flexibility due to the
scrapping/reducing of conventional (old) capacity.

G1 = value per unit market demand of the firm in volume at terminal time, T,
G2 = value per unit market demand of the firm in variety at terminal time, T,
G3 = value per unit capacity at terminal time, T,

G4 = value per unit of overall system flexibility at terminal time, T,

E(t) = e'il, a discounting factor, where i is a discount rate,

T = a terminal time of the planning horizon.

4.2 THE OBJECTIVE FUNCTION

The overall objective of the model is to maximize a SBU's strategic value,
including discounted values of its market share and technological capabilities at the
terminal time, while subtracting the total tangible and intangible costs over the planning
horizon. The dual-criterion objective fiJnction depicted in equation (4.1) addresses the
trade-offs between two competitive priorities -- cost efficiency and flexibility. This
function includes both tangible costs and intangible costs related to a system's flexibility.
Tangible costs include both the capital investment cost for the acquisition of automation
capacity and flexibility of new technology and the production variable costs. Intangible
costs include the costs of over-flexibility and under-flexibility represented by the difference
between achieved flexibility and required flexibility in terms of both volume and product
(part) variety. Also included in the objective fiinction are the costs related to the
discrepancies between actual demand level and business goal level in terms of both volume

and product variety. Minimization of these costs guides the acquisition decision for new

 

74

advanced technology to maintain the consistency between the firm's strategic focus and its

technology acquisition decision as a part of functional strategy.

Max [le(T) + szm + 63km + Gis(T)1E(t) — like? + sz2 + <2er + (bi + (4. 1)

bz)p+ c4(p— V)2 + c5(q — m)2 +<-31(v—V)2 +ez(m — M)2}Edt

The salvage values, G1v(T), G2m(T), G3k(T), and G4s(T), correspond to the
market valuation of the firm's market share in volume and variety and the valuation of the
capability of the firm's production technology at the terminal time. These values also
provide linkages for the continuation of the firm beyond the planning horizon (see also
Roth et a1. 1991).

The first term, claz, represents the acquisition cost for automation capacity of new
technology at time t. The term, czfQ, is the acquisition cost for flexibility of new
technology at time t. The term, c3r2, is the cost for scrapping or reducing conventional
(existing) capacity. The model represents the costs for acquiring automation capacity of
new technology and its flexibility and scrapping/reducing conventional capacity as
quadratic9 since it is assumed that large acquisition of new technology is more difficult to
assimilate into the existing system than the smaller acquisitions (see also Gaimon 1990;
Farley et al. 1987; Roth et al. 1991). With the same analogy, it is assumed that the larger
the conventional capacity scrapped/reduced, the more destructive it is for the production
system to reorganize. The total variable costs are denoted as the multiplication of
(b 1+b2), the sum of the average volume-related variable cost and the average variety-
related variable cost, by p ,the actual production level in units. These first four terms in
the integration are the tangible costs, which are directly related to cost efficiency as a

strategic priority.

 

9 For the purpose of simplicity, the quadratic cost functions for the technology acquisition and
attrition are assumed as found in the literature (see studies of Gaimon and Roth et a1). For the sensitivity
analysis as the extension of this study, it may be possible to assume different cost functions.

 

75
The next two terms in the objective function are the costs related to the system s
flexrbllity in terms of both lead time and product variety. The first term c4(p v)2 rs the
cost for over flexibility and under-flexibility of product volume flexibility represented
by the squared difference between actual production volume and market demand in
volume The second term c3(q - m)2, is the cost for over-flexibility and under flexibility
of product mix flexibility, described as the squared difference between the number of
products (parts) types produced by the system as achieved product (part) rnrx flexibility
and the number of products (parts) types demanded by the market as requrred product
(part) mix flexibility.

The last two terms, e1(v-V)2 and e2(m—M)2, are the costs related to the firm 3
strat eOic consistency depicted as the discrepancy between the firm 5 actual market posrtron
and the desired market position of the strategic business unit (SBU). In thrs model both
desired level of volume and desired level of variety as business goals V and M are
aSSIJI‘ITLed to be detemrined exogenously according to the firm 5 competrtrve strateoy and
the p Fojection of future market conditions as discussed in section 3.1 A srmrlar quadratic
representation like these two terms can be found in the model developed by Roth et a1

“9 9 1 ) (see also Holt et al. 1960; Bergstorm and Smith 1970; Chano and Jones 1970)
Roth et a1 (1991) explain the term in their model as the squared difference between actual
market share and desired market share as the SBU's goal such that "when the actual
market position exceeds the goal, the firm incurs a penalty cost because its oroanizational
Sim QtLlre and resources are strained. On the other hand, an opportunity cost is incurred
Wh e 1'1 the firm underachieves its goal level of demand. Any devratron from the desired

bu ‘
1 hess coal has a Significant impact on the firm's competitiveness and lone term survrval

et al.1991) Unlike the model of Roth et al., this model considers not only the sales

V6 1
1‘ll‘lle but also the product (part) variety as the elements of market position. The firm' 5

ab 1 1
lty to introduce more products to the market is directly related to the firrn' s

"In Q
Vativeness, which rs another key element for the firm' 5 competitiveness and long term

¥

 

76

survival. Hence, the last two terms in the objective fiinction measure the firm's strategic
competitiveness in two dimensions of both product volume and product variety.

As described above, the objective fianction of this model consists of three parts,
tangible costs combining fixed investment costs and production variable costs, costs
related to the system's flexibility, and costs related to the firm's strategic competitiveness.
The relative importance recognized by a firm can be accommodated by manipulating the
values of cost coefficients for three parts. Particularly, the trade-offs between cost
efficiency and flexibility can be explored by changing the values of cost coefficients for

flexibility, c4 and c5, in the objective fimction. Technically, the small values of cost

 

coefficients for flexibility, c4 and c5 , relative to those for tangible costs, cl, c2, c3, b1, and
b2, means the firm's strategic priority is focused on cost efficiency. On the contrary, the
large values of cost coefficients for flexibility relative to those for tangible costs means the
firm's strategic priority is focused on flexibility. Hence, depending upon the firm's
strategic focuses according to the values of these coefficients the firm will make different
optimal acquisition decisions for new advanced technology. To reflect the top priority of
the strategic "fit" emphasized by most manufacturing firms (see Fine and Hax 1985), the
values for cost coefficients of strategic competitiveness, el and e2, might be given the

highest values among the cost coefficients.
4.3 CONSTRAINTS
In this section, the constraints for state variables will be presented followed by the
upper bound conditions for control variables. The factors not included in the constraints

ar e assumed to be constant.

4 - 3- 1 Change in Market Demand

g

A firm may increase total demand in two separate ways. First, a firm can increase

 

77

the total demand by capturing the portion of the market share of its competitors through
the acquisition of new technology. Second, the total demand can be increased according
to the natural growth or decay of the market. As discussed previously, in this study the
market demand is viewed in two dimensions, volume and variety. Hence the state
equations for the change in market demand are depicted by equation (4.2. 1) and equation
(4.2.2) in terms of volume and variety respectively. As discussed in section 3.2, both
demand response fiinctions are formulated like the well-known Vidale-Wolfe sales
response model (Vidale and Wolfe 1957; see also Bass 1980). YV and Ym are total
market size in terms of volume and variety respectively, whereas v and m are the firm's

actual market demand in terms of volume and variety respectively.

 

v' = 7t1a(YV - v) + TCZV, v(0) = v0 (4.2.1)

The acquisition of automation capacity in equation (4.2. 1) serves as the market
stimulus. Since 7:1 represents the market responsiveness to the acquisition of automation
capacity, the term, rt1a(YV - v), denotes the fraction of the competitors' market share in
volume captured at any instant time due to the acquisition of automation capacity(see also
Kotabe and Murray 1990). The market growth/decay parameter, 7:2, in equation (4.2. 1)
characterizes the shape of the curve of the exogenous total market demand in volume of

the PLC. In this study, the firm's aggregate demand is assumed to follow the typical S-
Shaped curve according to the conventional PLC theory. Hence, the term, 7t2V, in
equation (4.2.1) denotes the instantaneous effect on the firm's demand in volume due to

the changes of the external market forces at a particular stage of PLC.
m' = ¢lf(Ym - m) + (bzm, m(O) = m0 (4.22)

The state equation for demand variety as depicted by equation (4.2.2) is very

k

78

similar to equation (4.2. 1) except for the market stimulus factor. As in equation (4.2.1),
the parameter, 6m, in equation (4.2.2) denotes the market responsiveness to the
acquisition of flexibility for increasing the firm’s product (part) variety of the market
demand. It may represent the customers’ perception for the finn's innovativeness to
introduce more new products (parts) in the market due to the improved flexibility (see
Swamidass and Newell 1987). The term, (Ym - m), represents product (part) types that
are available and unique in the market, but recognized by customers as product (part)
types not produced by the firm. Accordingly, the first term, ¢1f(Ym - m), denotes the
fraction of market product (part) variety captured by the firm at any instant time due to
the acquisition of flexibility. The parameter for natural market growth/decay in variety,
(b2, characterizes the changes in product (part) variety along the PLC curve (see also
Butler 1988). Hence, the term, (132m, represents the total exogenous change in the firm's
demand variety induced by other competitive factors than the acquisition of new
technology. According to the formulas given in equation (4.2.1) and equation (4.2.2),

both v and m are nonnegative for all te[O,T].

4.3.2 Change in Variable Costs
As discussed in section 3.3, the production variable costs are comprised of both
volume-related costs and variety-related costs. In equation (4.3.1), the parameter, 0L,
denotes the effectiveness in structural downward shift of the average volume-related cost
curve due to the acquisition of automation capacity. According to a study by Buzzell and
Wiersema (1981), the reduction in the per unit production and inventory cost is found
Proportionate to the level of the per unit production and inventory cost at time t (see also
Roth et al. 1991). The acquisition of automation capacity, a, causes this structural shift of
the volume-related cost. Accordingly, the term, - eta, represents the fraction of the
Volume-related cost, b1, to be reduced due to the acquisition of automation capacity at

time t. As defined before, BI is the lower bound of b1. Hence, b1 approaches

k

79

asymtotically B1 as t goes to infinity. Since new process technology has low level of
manual activities involved, the volume-related learning effect becomes insignificant (see

section 3.3). Hence, the volume-related learning effect is not considered in this model.

b1'=-0La(b1-B1), b1(0):b10 (4.31)
b2' 2 B(S'/S)b2, 132(0) 2 bzo (4.3.2)

The next state equation (4.3.2) depicts the change in the average variety—related
cost due to the acquisition of flexibility. The parameter, [3, denotes the proportionate
increase in the average variety-related cost due to the increase in product (part) variety
that the system processes. The term, B(s'/s), represents the fraction of the average
variety-related cost that changes according to the change in the product (part) variety
processed by the system, s, as illustrated in Figure 3.5. As discussed in section 3.3, the
term, s'/s, shows that the increase in the average variety-related cost is decelerated as the
cumulative level of overall system flexibility increases. According to the formulas given in
equation (4.3.1) and equation (4.3.2), it follows that both b1 and b2 are nonnegative for all

te[O,T].

4.3.3 Change in Production Capacity
The change in the production capacity in units of output, k', is depicted in equation
(4.4) as the net difference between the acquisition of automation capacity and the

scrapping/ reducing of conventional (existing) capacity.

k' = a - r, k(0) = k0 (4.4)

4.3.4 Change in Cumulative Capacity of New Technology

The change in total accumulated capacity of new technology is represented by the

80

acquisition rate of new capacity as expressed in equation (4.5). In the scenario analysis,
the initial value of x, x(0), determines the initial values of v, the firm's market demand in
volume, and b1, the average volume-related cost. According to equation (4.5), x is
nonnegative for all te[O,T]. This variable is included to calculate the cumulative

investment for automation capacity.

x' = a, x(0) = x0 (4.5)

4.3.5 Change in Overall System Flexibility

The overall system flexibility at time t is defined as the number of products (parts)
processed by the system at time t. The state equation (4.6) expresses the change in the
overall system flexibility due to the acquisition of flexibility. The parameter, p1, is the
effectiveness factor in assimilating the flexibility embedded in the acquired new technology
into the total production system to improve the overall system flexibility. This
effectiveness factor is quite similar to the synergy factor introduced by Meredith and
Cam (1989), which represents the synergy effect among different technologies to reduce
in the average production cost due to the acquisition of new technology (see also Roth et
al. 1991). Hence, p1 is a maximum system effectiveness representing the organization's
ability to assimilate the flexibilities of different technologies. The high value of p1 shows
the high effectiveness between the existing technology and the newly acquired automation
that realizes the full potential of newly acquired flexibility in improving the overall system
flexibility (see also Ettlie and Reifeis 1987; Schroeder 1990). However, the system's
flexibility can be reduced due to the scrapping/reduction of conventional or existing
capacity. It is assumed here that the system's overall flexibility decreases proportionately
by the scrapping/reduction of conventional or existing capacity. In this study, both p1 and

D; are assumed fixed.

 

81

S' = Plf' 92F , 8(0) 2 50 (4-6)

4.3.6 Inequality Constaints

According to the definitions of automation and flexibility in this model, the
acquisition of flexibility should be accompanied by the acquisition of automation. To
ensure this, the inequality constraint (4.7.1) is required, where P is a large number. This
constraint ensures the nonacquisition of flexibility required in the case of the
nonacquisition of automation. It also permits the case of the positive acquisition of
automation accompanied by the nonacquisition of flexibility, in which the acquired

automation merely increase the capacity of existing automation without any change in the

 

level of overall system flexibility. The constraint (4.7.2) depicts the relationship between
p, actual production volume and q, actual production in variety. The explanation for

constraint (4.7.2) is analogous to that for constraint (4.7.2)

Ha-fZO (4.7.1)
Wp - q 2 0 (4.7.2)
p gk_wq (4.73)
q S 5 (4.7.4)
k 2 0 (4.7.5)

The constraints (4.7.3) and (4.7.4) depict that acual production in volume and
variety, p and q, are bounded by actual production capacity in volume and variety,
repspectively. Note that actual production volume capacity will be reduced
proportionately as the number of product (part) types increases as shown in the right hand
side of the constraint (4.7.3). The last (4.7.5) is the non-negativity constraints for the

production capacity at time t.

82

4.3.7 Variable Boundaries

The upper and lower bounds for control variables are presented in equation (4.8).
The maximum rate of new technology capacity acquisition at time t may be constrained by
factors, such as budget (Gaimon 1985), the ability of the organizational infrastructure to
assimilate the new technology, and the availability of the technology (Roth et a1. 1991).
The factors affecting the maximum rate of flexibility acquisition may be similar to those for
new technology capacity acquisition. The upper bound on reducing existing old capacity
may depend on restrictions due to labor contracts, the ability of the organization to make
production process changeovers, and the impact of such changes on the organization
(Roth et al. 1991). The upper bounds for p and q, P and Q, are depicted by the

constraints (4.7.3) and (4.7.4).

a e (O, A), f e (O, F), r e (O, R), p e (O, P), and q e (O, Q). (4.8)

4.4 SUMMARY

In summary, the optimal control model presented in this chapter contains a multi-
criterion objective function to maximize the total value of a firm over the planning
horizon. The model has seven state constraints, six inequality constraints, and the

boundary constraints for the control variables. The whole model is depicted below.

Max [G1v(T) + sz(T) + G3k(T) + Gis(T)]E(t) — Ema? + c2f2 + c3r2 + (b1 +

b2)p+ c4(p- V)2 +c5(q - m)2 +el(v—V)2 + e2(m — M)2}Edt

subject to

v‘ = n1a(Yv- v) + thv,
m' = ¢1f(Ym-m) + <l>2m ,
b1' = ' (13031-3),

 

 

 

83

132' = B(S'/S)b2 ,

S':Plf'92F,
Ha-fZO,
WP-qZO,
psk-wq,
qu,

k20,

V(O) = V0,

 

m(O) = m0

b1(0) = b10>

b2(0) = bZO,

W» = k0,

x(0) 2 X0,

s(O) = so, and

a E (0, A), f E (O, F), r E (O, R), p E (O, P), and q E (O, Q)

CHAPTER V
SOLUTION APPROACH

This chapter presents both the analytical and the numerical solution approaches for
the optimal control model presented in Chapter IV. Section 5.1 discusses the analytic
optimal solutions for the acquisition of new technology and the scrapping/reducing of
conventional capacity. Section 5.2 describes the discretized non-linear version of the

model to solve with GRG2 in Microsoft Excel®.

5.1 OPTIIVIAL POLICIES

This section first displays the necessary optimality conditions from the Hamiltonian
equation (5.1) and the Lagrangian equation (5.2). Then it presents the optimal policies for
the acquisition rate of new technology and the scrapping/reducing rate of conventional

capacity.

5.1.1 Necessary Conditions

The Hamiltonian equation corresponding to the model presented in Chapter IV is
given in equation (5.1), where M, M, 7&3, I4, 15, X6, and k7 are the adjoint variables
related to the state variables, v, m, b1, b2, k, x, and s, respectively, and E is the

discounting factor as defined in section 4.1.3.210

g

10
variable.

The interpretation for each of the adjoint variables is the marginal value of its respective state

84

 

85
H = AIV' + sz' + A3b1' + A4132. + Ask. + A6X' + A75. -
{0132 + C2f2 + c3r2 + (b1 + b2)!3 + 04((P - CDZ +

Cs(q - m)2 + 6M - V)2 + eztm - M)2}E, (5.1)

Due to the inequality constraints in section 4.3.6, a Lagrangian equation is formed

as

L = H + m(Ha - f) + n2(Wp - q) + T13(k - wq - p) + m(s-q), (5.2)

where n1, n2, T13, and 114 are Lagrange multipliers. The last nonnegativity constraint for
the production capacity is not considered explicitly in the above equation. Later, during
the numerical solution procedure if r(t) - a(t) > k(t) occurs for any time t E [0,T] , then
the algorithm will modify the value of r(t) in order to make r(t) - a(t) = k(t) so that k(t) 2 O
is not violated.

The optimality conditions for the model are presented below. Equations (5.3)
through (5.9) are the same constraints as presented in Chapter IV with the initial values
for the state variables. Equations (5.10) through (5.21) are derived from the Lagrangian
equation (5.2) by taking the first derivatives with respect to the state and adjoint variables.
Detailed expressions for the conditions from (5.10) through (5.16) are shown in Appendix
I. The complimentary slackness conditions of (5.22)-(5.25) are needed for the inequality
constraints (4.7.1)- (4.7.4) to be satisfied. The necessary conditions and the

complementary slackness conditions for the optimality of the model are;

v' = 7t1a(YV - v) + nzv, V(O) = v0, (5.3)
m' = ¢1f(Ym-m) + (bzm, m(O) = m0 , (5.4)
b1’=-aa(b1-B), b1(0)=b10, (5-5)

b2'= B(S'/S)b2, b2(0)=b20, (5-6)

 

86

k'=a-r, k(0)=k0, (5.7)

x' = a, x(0) = x0 , (5.8)

S' = Prf- 92E 5(0) = 50, (5.9)

l1'= - ESL/5v, K1(T)= G1E(T), (5.10)
12' = - ESL/6m, M(T) = G2E(T) , (5.11)
7L3‘ = - 6L/5b1, 13(T) = O, (5.12)
M' = - oL/bbz, 14(T)= O, (5.13)
15' = - ESL/5k, 15(T) = G3E(T) , (5.14)
7&6' = - oL/bx, K6(T) = O , (5.15)
k7' = - ESL/55, M(T) = G4E(T) , (5.16)
6L/5a = O, for a = (O, A) , (5.17)
ESL/5f: O, for f= (O, F) , (5.18)
ESL/5r = O, for r = (O, R), (5.19)
6L/5p = O, for p = (O, P), (5.20)
ESL/Sq =0, forq=(0, Q), (5.21)
n120,Ha-f20,m(Ha-f)=0, (5.22)
712 20,Wp-t120m2(Wp-q)=0, (5.23)
n3ZO,k-wq-p20,n3(k-wq-p)=0,and (5.24)
11420, s-q20,n4(s-q)=0. (5.25)

5.1.2 Optimal Policies for Acquiring New Technology
In Theorem 1, the optimal policies for the control variables, a(t) and f(t), satisfying
both the necessary conditions and the complementary slackness condition in section 5.1.1,

are stated.

Theorem 1: The optimal policies for the acquisition rates of automation capacity and

flexibility are depicted below. According to the complementary slackness condition of

87

(5.22), there are two possible cases for the optimal policies for the acquisition rates of

automation capacity and flexibility.

Case 1. If Ha(t) - f(t) > 0, then n 1(t) = O and a(t) > O and f(t) 2 0.

Hence,

9,0), if0<6,(t)<A(t)
F(t), 'f9(t)2F(t)

f(t)={9f(t), if0f< 9f(t)<F(t) (5'27)
0 lf6f(t)SO

a(t) : { AU), if Ha(t) 2— Am and (5.26)

, where
9a(t) = {M(t)7tr[Yv-V(t)l - 13(t)0t[br(t)-Bl
+ M(t) + X60) + m(t)H}/261E(t) (5.28)

and
9r“) = {k2(t)¢1[Ym-m(t)] + 7&4(t)3915'1(t)b2(t)
+ X7(t)91 ' Tlr(t)}/202E(t)- (529)

Case 2. If Ha(t) - f(t) = 0, then m(t) 2 O and both a(t) and f(t) = 0.
Hence,
if 93(t) .<_ O with m(t) being ignored,
a(t) = O and f(t) = O regardless of the value of 9f(t).

Theorem 1 assures that the acquisition of flexibility should be accompanied by the
acquisition of automation capacity. Here, equations (5.28) and (5.29) are the solutions of
necessary conditions (5. 17) and (5.18) with respect to a(t) and f(t), respectively. When
9a(t) is positive, Case 1 holds. In Case 1, although the firm purchases automation
capacity, it may or may not purchase the flexibility depending on the value of the
numerator in equation (5.29). When 93(t) is not positive, then Case 2 holds. In Case 2,

a(t) becomes 0 and f(t) also becomes zero regardless of the value of 91(t) due to the

88

complementary slackness condition (5.22). Hence, when there is no acquisition of
automation capacity there should be no acquisition of flexibility.

Ignoring the Lagrange multiplier m(t), the interpretation of the optimal policies
for the acquisition of new technology should be focused on the adjoint variables in
equation (5.28) and equation (5.29). First, in equation (5.28), the acquisition of
automation capacity depends on the combination of four marginal values associated with:
(1) additional unit of demand in volume due to the acquisition of automation capacity, )\.
1(t)1t2(Yv-v(t)); (2) the reduction of average volume-related cost due to the acquisition of
automation capacity, - k3(t)a[b1(t)-B]; (3) a unit increase in production capacity, K5; and
(4) a unit increase in automation capacity, 16. Hence, the acquisition rate of automation is
the net marginal contribution to the objective fianction of a unit increase in automation
capacity at time t divided by its discounted purchase cost, 2c1E(t).

It is interesting to note that, even though the marginal value of increasing
production capacity or automation capacity, its or k6, is negative in equation (5.28), it is
still possible to purchase automation capacity due to the other considerations, such as
demand increase in volume and lowering production costs. In particular, as seen in the
first term of the numerator, total market size, YV, as an exogenous variable, can have an
impact on the acquisition of automation capacity. This means the firm's acquisition policy
of the automation capacity depends on the external market conditions, as discussed in
Chapter 111.

As depicted in equation (5.29), the acquisition of flexibility depends on the
combination of three marginal values associated with: (1)unit increase of demand in
variety due to the acquisition of flexibility, K2(t)¢1(Ym-m(t)); (2) increase in the average
variety-related cost due to the acquisition of flexibility, X4(t)Bpls‘1(t)b2(t); and (3)
increase in the overall system's flexibility due to the acquisition of flexibility. Hence,
equation (5.29) represents the acquisition rate of flexibility as the net contribution to the

objective function of the additional unit purchase of flexibility at time t divided by its

 

89

discounted purchase cost, 2c2E(t). As for the acquisition of automation capacity in
equation (5.28), the total market sizes in volume and variety, Yv and Ym, in equation
(5.29) also affect the acquisition of flexibility.

Hence, the existence of exogenous market variables in equation (5.28) and
equation (5.29) suggests that the firms should determine the acquisition policy for the new
technology with due consideration of potential market growth or decay in the future. In
fact, it would be much easier to increase a firm's market share through the acquisition of
new technology in a market with a growing phase than in a market with a declining phase.

In the latter case, the investment in new technology at the early stage with a wrong market

 

forecast might cause a serious financial strain to the firm with actual aggregate sales
declining in the fiiture and, consequently, result in the loss of the firm's competitive
strength in the long run. This is why, as the model suggests, the decision making process
for the acquisition of new technology should consider external market conditions along

with the firm's strategic priority.
5.1.3 Optimal Policy for Reducing Conventional Capacity
In Theorem 2, the optimal policy for the scrapping/reducing rate of conventional

capacity resulting from the optimality condition (5.19) is presented.

Theorem 2: The optimal scrapping/reducing rate of conventional capacity is

a(t), if 0 < a(t) < R(t)

{M0, ifR(t)S (Mt) (5.30)
r(t):
0 , if 9.(t)s0

, Where 9:0) = - [M(t)l3925'1(t)b2(t) + M(t) + 7k7(t)t32]/ 293130) (5.31)

Theorem 2 states the scrapping/reducing rate of conventional capacity is

determined as the policy shown in equation (5.30). Equation (5.31) is the result of solving

9O

necessary condition (5.19) with respect to r(t). According to equation (5.31), the rate of
scrapping/reducing conventional capacity depends on three marginal values associated
with: (1) decrease in average variety-related costs due to unit decrease in conventional
capacity, - K4(t)szs‘1(t)b2(t); (2) decrease in production capacity due to unit decrease in
conventional capacity, - M(t); and (3) decrease in overall system flexibility due to unit
decrease in conventional capacity, - k7(t)p2. Hence, the scrapping/reducing rate of
conventional capacity is determined as the ratio of the sum of three marginal values to the
discounted cost of the reducing conventional capacity as shown in equation (5.31). Note
that the firm would reduce the conventional capacity as the marginal value of additional
production capacity, M(t), is negative and the others are nil in equation (5.31). Therefore,
it is possible to see the acquisition of new technology and the scrapping/reducing of
conventional capacity at the same time since the new technology can be purchased

although 3.5(t) is negative as discussed in section 5.1.2.

5.1.4 Optimal Policies for Production Rates
Theorem 3 provides two optimal policies for the rate of production, one for

production volume and another for production variety.

Theorem 3: The optimal policies for the rate of production are depicted below.
According to the complementary slackness condition of (5.23), there are two possible

cases for the optimal policies for the rate of production.

Case 1: If Wp(t) - q(t) > 0, then m(t) = O, p(t) > 0, and q(t) 2 0

Hence,

_ P(t), ife (t)ZP(t) (5.“2)
P“) ‘ {9pm, if 0p<9p(t)<P(t) ’ and J

 

91

Q(t), ifeqmzcm (5.33)
q(t) = 0q(t), if0<9q(t)<Q(t) ,
o, if0q(t)SO

where

9130) = [(204V(t) - but) ' 92(0)]30) + T120)W - MUN/20450) (5-34)

and

9q(t) = (205m(t)E(t) - m(t) - TRON - MW 5295130)- (535)
Case 2: If Wp(t) - q(t) = 0, then m(t) 2: 0, p(t) = 0, and q(t) = 0.

Hence, if 0p(t) s 0, p(t) = 0 and q(t) = 0 regardless of the value of 0q(t).

Theorem 3 states the optimal policies for prodution levels in terms of volume and
variety are determined as shown in (5.32) and (5.33). Equation (5.34) and (5.35) are the
solutions of necessary conditions (5.20) and (5.21) with respect to p(t) and q(t),
respectively. Explanation for Case 1 and Case 2 of Theorem 3 is similar to that of
Theorem 1. Case 1 explains that no production in variety is possible although there is
positive production in volume. Case 2 explains the fact that no production in volume
should accompany no production in variety.

Ignoring Lagrange multipliers, equations (5.34) and (5.35) state that production
level in volume is determined by V(t), the actual demand in volume, and production level
in variety by m(t), the actual demand in variety. Furthermore, according to the constraint
(4.7.3), production level in volume is limited by k-wq, effective production capacity in
volume. Production level in variety is also constrainted by s, the level of system’s
flexibility according to the constraint (4.7.4). Therefore, P(t) and Q(t), the upper bounds
of p(t) and q(t), might have different values at any time t depending upon whether the
constraints (4.7.3) and (4.7.4) are satisfied or not.

If the values of 0p(t) and 0q(t) violates any of the constraints (4-7.3) and (4.7.4)

when both m(t) and n2(t) are ignored, the values of Lagrange multipliers for violated

 

92

constraints become positive and the optimal values for p(t) and q(t) have to be modified

accordingly.
5.2 SOLUTION ALGORITHM

5.2.1 Discretized Non-Linear Version of the Model

Since the closed form solutions for the model can not be obtained due to the
dynamic interactive behavior of the state, control, and adjoint variables, it is approximated
through the conversion to a discrete-time non-linear problem. The converted problem is
solved using GRG2 algorithm with the MicroSoft Excel®. The discrete-time non-linear

problem is presented below.

Max [G1v(T) + G2m(T) + G3k(T) + G4s(T)]E(T) — :kclazU) + czfz(t)+ c3r2(t)

+ [bi + b2]p(t)+ C4[p(t)- V(t)]2 + Cs[q(t)- m(t)]2 + er[V(t)- V(t)]2 (5.36)
+ ez[m(t)- M(t)]2}E(t)
subject to
v(t+1) = V(t) + At(7t1a(t)[YV - V(t)] + 7tzv(t)), for t= o, 11 (5.37)
m(t+l) = m(t) + At(¢1f(t)[Ym-m(t)] + ¢2m(t)) , for t= 0,.., T-l (5.38)
b1(t+1) = b1(t) - At(oca(t)b1(t)), for t= o, 111 (5.39)
b2(t+l) = b2(t) + At(B[s'(t)/s(t)]b2(t)), for t= o, T-l (5.40)
k(t+1) = k(t) + At(a(t) - r(t)) , for t=0, T-l (5.41)
x(t+l) = x(t) + Ata(t) , for t=0, T-l (5.42)
s(t+l) = s(t) + At(p1f(t) - p2r(t)), for t=0, T-l (5.43)
Ha(t) - f(t) 2 o , for t=0, T-l (5.44)
Wp(t) - q(t) 2 0 , for t=0, T-l (5.45)

p(t) s k(t) _ \Vq(t) , for t=0, T-l (5.46)

93

q(t) s s(t) , for t=O, T-l (5.47)
k(t) 2 o, for (=0, T (5.48)
V(O) = V0, (5.49)
m(O) = mg, (5.50)
b1(0)=bloa (5-51)
b2(0) = b20» (5-52)
k(O) = k0, (5.53)
x(0) = x0, (5.54)
s(0) = so, and (5.55)
a(t)€[0. 130)], f(t)E[0, Fm], r(t)€[0, Rm],

P(t)€[0, P(t)], and Q(t)€[0, Q(t)] (5.56)

In the preliminary study, the problem with base scenario is actually solved by
taking the time step, At, in Equation (5.37) through (5.43) as both .5 and 1. It is found
that the solutions with the time step of .5 give smoother results for the optimal values of
control variables than with the time step of 1 as they are plotted in graphs, but the
solutions in terms of the pattern of acquisition policy are quite insensitive to the values of

time step.

5.2.2 Limitations

Since the solution of the non-linear problem by any commercial algorithm available
right now usually depends on the initial values of decision variables, the solution process
inevitably has to follow a trial and error procedure. To find whether the solution at a
certain step is a local optimum, the values of control variables at that step are carefully
checked to find whether there is some room for improvement in the objective function by
the changes in the values of control variables without violating the constraints. If the

solution is found to be a local optimum, the values of control variables are modified and,

94

then, the problem is re-run. With this kind of iterative procedure, most of the problems
have been solved after several iterations.

The major limitation regarding this solution process is that the solutions are near
optimal due to the conversion of the original continuous-time model to a discrete-time
non-linear one. Since there is no closed form solution for this problem, this is the only
option to find the optimal solution. The second limitation is a generic problem with most
of solution algorithms for non-linear problems, which can not guarantee the global
optimum. Since there is no theoretical background to find the global optimum, some of
the solutions presented in this dissertation might be local optimum. However, since the
objective fiinction of the model here is convex, it can be expected that most of the
solutions presented later in Chapter 6 would be global optimum.

The last limitation is the value of At which is considered as 1 in this study. Due to
the capacity of the solution algorithm it was not allowed to have a small size of the time
step. If a solution algorithm which can handle a large problem more experiments will be

conducted with different sizes of the time step.

CHAPTER VI
SCENARIO ANALYSIS

In this chapter scenario analysis with hypothetical settings is conducted. The first
section of this chapter presents a general description of the experimental design, and the
second section presents the results of the scenario analysis and discusses the strategic and
managerial implications of the effects of research factors on the optimal acquisition of

automation capacity and flexibility.

6.1 OVERVIEW OF DESIGN FOR SCENARIO ANALYSIS

This section is an overview of experimental factors, the settings of factors, the
level and value settings for their related variables for the scenario analysis. The strategic
choice and the PLC as major experimental factors are explained in Section 6.1.1. In
Section 6.1.2 initial process technology as a structural factor is explained. In Section
6.1.3 the level and value settings of research variables related to the experimental factors
are presented. In Section 6.1.4 the value settings for other exogenous variables are
explained. In Section 6.1.5 the hypothetical PLC curves used in the scenario analysis are
discussed. In Section 6.1.6 the frameworks of the design for the scenario analysis are

presented. Finally, in Section 6.1.7 the performance measures are explained.

6.1.1 Strategic Choices and The PLC

Major research factors identified in the literature review are the stage of the PLC
and the choice of a firm's strategic priority (see Table 6.1 and Table 6.2). Table 6.1
depicts possible strategic choices of the combination of strategic priorities which a firm
can pursue when the market behaves according to the evolution of conventional PLC

theory from the introductory stage (I), to the growth stage (G), to the mature stage (M).
95

96

In Table 6.2 the possible strategic choices when the market behaves according to the
theory of the reversal of the PLC are presented. In this scenario analysis the declining
stage of the PLC is not included since this stage does not match with any point of the
horizontal axis in the product-process matrix. Furthermore, each firm, as continuous
business entities, may have different strategies for the existing production capacity at this

stage according to the type of industry in which it is operating.

Table 6.1
Strategic Choices and The Normal PLC

 

 

 

 

Evolution of PLC“
Strategic
Choice
l G M
Flexibility/

1-1 Flexibility Cost Efficiency Cost Efficiency

1-2 Cost Efficiency >

1-3 Flexibility

 

 

 

 

 

' l stands for introductory stage, (3 stands for growth stage, and M stands for
maturity stage.

In Table 6.1 the possible choices of the combination of strategic priorities during
the evolution of the PLC are given: (1) choice l-l, in which a firm's strategic priority is
changing from flexibility for the introductory stage, and flexibility/cost efficiency for the
growth stage, to cost efficiency for the mature stage; (2) choice 1-2, in which the strategic
priority is always cost efficiency; and (3) choice 1-3, in which the strategic priority is
always flexibility. Choice 1—1 is the one generally supported by the literature (Hayes and
Wheelwright 1984; Hill 1980).

In Table 6.2 the PLC is assumed to evolve from the introductory stage (I), through

the growth stage (G), and then back to the growth stage (RG) again. The possible

strategic choices are (1) choice 2-1, which is the same as choice 1-1 in Table 6.1; (2)
choices 2-2 and 2-3, which are the same as choices 1-2 and 1-3, respectively; and (3)

choice 2-4, which is similar to choice 1-1 except that it includes flexibility for the RG

stage.

97

Table 6.2

Strategic Choices and the Reversed PLC

 

Strategic
Choice

Reversal of PLC

 

G RG'

 

2-1

2-2

2—3

2—4

 

 

Flexibility
Cost Efficiency
Flexibility

Flexibility

F lexibility/

 

 

Cost Efficiency Flexibility
p
>
Flexibility! Flexibility

Cost Efficiency

 

" RG stands for reversed growth stage.

6.1.2 Positioning of Initial Process Technology

There are two structural variables related to the positioning of initial process
technology: (1) so, the initial level of flexibility and (2) Xo, the initial level of automation.
As shown in Figure 6.1, there are four types of initial process technology with different
combinations of so and Xo; (1) Firm A with high so and low Xo; (2) Firm B with high so
and high Xo; (3) Firm C with low so and low Xo; and (4) Firm D with low so and high

Xo. In Figure 6.2 the combination of levels of these two variables determines a firm's

initial process technology in the technology matrix.

 

98

 

 

 

 

 

 

F

L A B

E “'9“ High 30 High 50

I Low xo High x0

8

|

L C D

| Low

T Low 80 Low 80

Y Low X0 High X0
Low High

AUTOMATION

Figure 6.1 Four Types of Initial Technology Choice

 

 

F ng’l
L
E
X
I
B
l
L
l
T
. 0
Low
Low Autanation Higi Allorration

AUTOMATION

Figure 6.2 Positioning of Initial Technology Choice

According to the normative concept of the Product-Process Matrix, Firm A, with
high flexibility and low economies of scale, is suitable for the production of products of
high variety and low volume. On the contrary, Firm D, with low flexibility and high
economies of scale, is suitable for the production of products of low variety and high
volume. Firm B, with high flexibility and high economies of scale, can operate efficiently
in a wide span of operating environments from the high variety/low volume stage to the

low variety/high volume stage. Firm C is also included in the analysis, although this has

99

not been considered as a practical setting in a static environment. However, there is yet
no conclusive theory for the best initial technology choice in a dynamic environment such

as the one in this study.

6.1.3 Settings of Variables

The values of some exogenous variables as defined in section 4.1.3.2 change
according to the different combination of levels of the factors discussed in section 6.3.1.
In Table 6.3 the variables whose settings are changing according to the evolution of the
PLC include (1) Yv(t), the total market size in volume; (2) Ym(t), the total market size in
variety; (3) 7:2, the growth/decay factor of a firm's market share in volume; and (4) (1)2, the
growth/decay factor of a firm's market share in variety. The settings for YV(t) and n2 are
the same for all stages of the PLC. The settings for Ym(t) and (1)2 are also the same for all
the stages of PLC. This is due to the fact that both 7:2 and ()2 follow the behavior of the

total market size according to the evolution of the PLC.

Table 6.3

Variable Settings for the Stages of the PLC

 

 

 

Stages of PLC
Variables
I G M RG
Yv(t) MG FG so MG
let) FG FD so so
1!: MG FG SG MG
4), FG FD so so

 

 

 

 

' F stands for fast. M for medium. S for slow, G for growth, and D for decline

100

The settings of Yv(t) —- the total market size in volume -- are (1) medium growth
at the introductory stage; (2) fast growth at the growth stage; (3) slow growth at the
mature stage; and (4) medium growth at the reversed growth stage. The settings for
Ym(t) -- the total market size in variety -- are (1) fast growth at the introductory stage; (2)
fast decline at the growth stage; (3) slow decline at the mature stage; and (4) slow growth

at the reversed growth stage.

Table 6.4

Variable Settings for Strategic Priorities

 

Strategic Priority

 

Variables ' ' '
Flexibility C°5t Efficiency /Co:tleExflfti)cl:lieyncy

 

C4 H' L M
Cs H L M
W) L H M
M(t) H L M

 

 

 

 

As depicted in Table 6.4, variables related to the strategic priority are (1) C4, costs
for inflexibility of lead time; (2) c5, costs for inflexibility of variety; (3) V(t), the business
goal of the market share in terms of volume; and (4) M(t), the business goal of the market
share in terms of variety. The settings of C4 and c5 are (1) high values for C4 and 05 when
a firm's strategic priority is flexibility; (2) low values for C4 and C5 when a firm's strategic
priority is cost efficiency; and (3) medium values for C4 and c5 when a firm's strategic
priority is flexibility/cost efficiency. The settings of V(t) are (1) low for the priority of
flexibility; (2) high for the priority of cost efficiency; and (3) medium for the priority of
flexibility/cost efficiency. The settings of M(t) are (1) high for the priority of flexibility;

101

(2) low for the priority of cost efficiency; and (3) medium for the priority of flexibility/cost

efficiency.

Table 6.5

Value Settings of Research Variables
Related to the Strategic Choice

 

 

 

 

 

High Medium Low
C4 100 50 25
C 5 200 100 50
“v .002 .001 .0005
um .01 .005 .0025

 

The values corresponding to the levels of research variables related to the strategic
choice are depicted in Table 6.5. The values of C5 are twice that of C4 at each level to
reflect the difference in the acquisition cost between automation capacity and flexibility.
The value settings for V(t) and M(t), the SBU's goals of the market share in terms of
volume and variety, are represented by the growth factors -- Liv and Hm -- in the functions
defined below. It is assumed that

V(t) = Yv(t) (c + ttv(t-i)) and

Mt) = Ym(t) (d + umrt-i».
where both C and d are .l at time 0 and represent the firm‘s market share at the starting
time of each stage of the PLC, and i is the index of the initial period of each stage of the

PLC.

102

As depicted in Table 6.6, the variables related to structural factors -- so and x0 --
are (1) blo, the initial average volume—related cost and (2) b20) the initial average variety-
related cost. The settings of these two variables are (1) high b10 and low bzo for Firm A;

(2) low b10 and low bzo for Firm B; (3) high blo and high b20 for Firm C; and (4) low blo

and high 1320 for Firm D.

Table 6.6

Variable Settings for the Positioning of Initial Technology Choice

 

 

Initial Production Cost
Initial Technology Choice V olume- V ari ety-
x. .. “flit?" Riéiii“
A L H H H
B H H L H
C L L H L
D H L L L

 

 

 

 

" H stands for high and L for low

Table 6.7

Value Settings of Research Variables
Related to Initial Process Technology

 

High Low

 

X0 (bio) 150 (25) 50 (30)

SO (b20) 100 (15) 10 (10)

 

 

 

 

103

The value setting for these two variables are presented in Table 6.7. The values in
parentheses are the initial values for average volume- and variety- related variable costs --
blo and b20 -- corresponding to each level of xo and so. These values are determined after
the preliminary runs. The high value of bio and b20 are set arbitrarily at 30 and 10 with xo
at 50 and so at 10. After running several problems with different settings of the other
variables, the values of b10 and b20 corresponding to the values of x and s, 150 and 100
are interpolated from the solutions. As a result, the value for b10 is 25 when xo is 150 and

the value of b20 is 15 when so is 100.

6.1.4 Values of Exogenous Variables

The values of the base scenario is presented in Table 6.8. Most of the values in the
base scenario are similar to those in the study by Roth and Gaimon (1991). However,
there are some exogenous variables unique to this model. The values of these exogenous
variables are set after some experimentation with various levels for each one.

The values of G1, G2, G3, and G4 -- the marginal values at the terminal time of
the firm's market share in volume, V(T), the firm's market share in variety, m(T), the
capacity in volume, k(T), and the system's flexibility, s(T) -- are set at 300, 600, 50, and
100, respectively. The values of C 1, C2, and C3 -- the acquisition cost of automation and
flexibility, and the scrapping cost of existing or conventional capacity -- are set at 50, 100,
and 20, respectively. The cost of flexibility, C2, is set as twice the cost of automation, C1,
in order to reflect the fact that the flexible system costs an enormous amount compared to
a mere automated system such as the stand-alone NC machine.

The values of E1 and E2 -- the costs related to the strategic fit between business
strategy and corporate strategy -- are set at 1000 and 2000, respectively. These values are
larger than others because of the high priority given to the fit among strategies in different

levels. During the preliminary runs, these two values have been found as major factors

104

influencing the effectiveness of initial process technology on the optimal policy for

technology acquisition. This will be further discussed in detail later in Section 6.2.

Table 6.8

Value Settings for Exogenous Variables

 

 

Parameters Value
G] 300
G2 600
(33 50
G4 100
C] 50
C2 100
(:3 20
E] 1000
E2 2000
R] .00002
9] .0005
or .002

[3 1
B 1
Di 8
pz .1
MA 200
ME 40
MR 20
\l/ .5
H l .0E+10
W 1.0E+10

 

 

 

 

The values of the market responsiveness factor, 7:1 and (b1, are set at .00002 and
.0005, respectively. It has been found that these two values have a significant impact on
the overall cost performance of each problem during preliminary runs (see also Roth and
Gaimon 1991). Since the purpose of this dissertation is not to study the sensitivity

analysis of research parameters with the model here, the values of these factors are

105

determined at reasonable levels after Close scrutiny of the results of the preliminary runs.
The values of effectiveness factors for the variables costs, or and B, are set at .002 and .1,
respectively. It has been found that these two factors have a significant impact on the size
of investment of both automation capacity and flexibility. However, they are not affecting
the general pattern (policy) for the acquisition of automation capacity and flexibility.

The minimum value of the volume-related cost is assigned at l. The effectiveness
factors for the system's flexibility, p1 and p2, are set at .8 and .1, respectively. Within the
reasonable range of the values for these two factors, there has been no significant impact
on the Optimal solution with the base scenario. The upper bounds for the control variables
—- a(t), f(t), and r(t) —- are set at 200, 40, and 20, respectively. The value of w, the
effectiveness factor of scrapping existing capacity(r(t)) on the total capacity on
volume(k(t)), is set at .8. This factor is also found to be an insignificant factor affecting

the optimal solutions. The values of H and W are set at both l.0E+lO as a large number.

6.1.5 Hypothetical PLC Curves

The difference between the normal PLC curve and the reversed PLC curve has
already been discussed in Chapter II. Tables 6.9 and 6.10 depict the discretized values for
1:2 and (192 for the normal PLC curve and the reversed PLC curve. n2 defines the shape of
the total market size of volume, and (b2 the shape of the total market size of variety. The
shapes of the PLC curves used for the scenario analysis are illustrated in Figures 6.3
through Figure 6.6. The curves in Figure 6.3 and Figure 6.5 depict the aggregate demand
in units of output by combining the market demand of all products in a family. The total
market demand of variety is also the aggregate demand of all products in the same family.
It is assumed that

Yv(t) = Yv(0)(a+32(t-i>>, Yv(0)=1000, and

Ym(t)= Ym(0)(b+¢2(t'i)), Ym(0)=100,

106

where both a and b are the exponents for the values of YV and Ym at the starting time of

each stage of the PLC, both 7:2 and (1)2 are the growth factor of YV and Ym determined

exogenously, and i is the index of the initial period of each stage of the PLC.

 

 

 

 

 

 

 

 

 

 

Table 6.9
The Values of Variables
of the Normal PLC
n2 (1)2 YV Ym

Introductory 0 .02 .20 1000.0 100.0
stage 1 .04 .50 1020.0 120.0
2 .10 .70 1060.8 180.0
3 .20 .30 1166.9 306.0
Growth 4 .30 .10 1400.3 397.8
Stage 5 .30 .01 1820.3 437.6
6 .30 -.01 2366.4 441.9
7 .30 -.10 3076.4 437.5
8 .30 -.40 3999.3 393.8
9 .20 -.30 5199.1 236.3
10 .10 -.20 6238.9 165.4
11 .05 -. 10 6862.7 132.3
Mature 12 .03 -.05 7205.9 119.1
Stage 13 .02 -.01 7422.1 113.1
14 .01 -.01 7570.5 112.0
15 .01 -.01 7646.2 110.9
16 .01 -.01 7722.7 109.8
17 .01 -.01 7799.9 108.7
18 .01 -.01 7877.9 107.6
19 .01 -.01 7956.7 106.5
20 .01 -.01 8036.2 105.4

 

The shape of the normal PLC curves can be found ubiquitously in the literature.

The shape of the reversed PLC curves as in Figure 6.4 and Figure 6.6 are yet unpopular.

 

107

Generally it is argued that the demand of volume after the growth stage of the reversed

PLC continue to grow without entering into the maturity stage, and also the demand of

variety continues to rise after the growth stage (Albernathy et al. 1983; Ayres and Steger

1983)

 

 

 

 

 

 

Table 6.10
The Values of Variables
of the Reversed PLC
7:2 <l>2 Yv Ym

Introductory 0 .02 .20 1000.0 100.0
Stage 1 .04 .50 1020.0 120.0
2 .10 .70 1060.8 180.0
3 .20 .30 1166.9 306.0
Growth 4 .30 .10 1400.3 397.8
Stage 5 .30 .01 1820.3 437.6
6 .30 - 01 2366.4 442.0
7 .30 - 10 3076.4 437.5
8 .30 -.20 3999.3 393.8
9 .20 -.10 5199.1 315.0
10 .10 -.05 6238.9 283.5
11 .05 -.01 6862.7 269.3
Reversed 12 .01 .01 7205.9 266.7
Growth 13 .05 .05 7277.9 269.3
Stage 14 .05 .10 7641.8 282.8
15 .05 .10 8023.9 311.1
16 .05 .10 8425.1 342.2
17 .03 .05 8846.4 376.4
18 .02 .03 9111.8 395.2
19 .01 .01 9294.0 407.1
20 .01 .01 9387.0 411.1

 

 

 

 

 

 

108

9000 --
80m ..
7000 -

Total Market Size in Volume

 

 

Time

Figure 6.3 Changes in Total Market Volume of the Normal PLC

8
o

8
o

8
o

200 -»

 

100 --

Total Market Size in Variety

 

 

Ott‘fttittrkitttiitf.
1 4 7 101316 19

Time

Figure 6.4 Changes in Total Market Variety of the Normal PLC

109

10000 ~-
9000 -
8000 ~
7000 -
6000 '
5000 -
4000 '
3000 -
2000 -
1000 -

Total Market Size in Volume

 

 

 

Figure 6.5 Changes in Total Market Volume of the Reversed PLC

8
c>

‘8

300 --

200 ‘-

100 --

Total Market Size in Variety

 

 

Figure 6.6 Changes in Total Market Variety of the Reversed PLC

6.1.6 Frameworks of Design

There are two frameworks of design for the scenario analysis according to the type
of PLC. Table 6.11 presents a design framework with the normal (conventional) PLC,
whereas Table 6.12 presents a design framework with the reversed PLC. There are two
factors for the design (1) strategic Choice and (2) initial technology choice. In Table 6.11

there are three levels of the first factor: (1) choice 1-1; (2) choice 1-2; and (3) choice 1-3.

110

In Table 6.12 there are four levels of the first factor: (1) Choice 2-1; (2) Choice 2-2; (3)
Choice 2-3; and (4) Choice 2-4. There are the same four levels of the second factor in both
Table 6.11 and Table 6.12: (1) Firm A; (2) Firm B; (3) Firm C; and (4) Firm D. The
number of replications of the first design, as depicted in Table 6.11, is twelve, whereas for
the second design there are sixteen replications. Therefore, the total number of
replications for the scenario analysis is twenty-eight. This number will increase
exponentially as more variables are included for other sensitivity analyses. The actual

settings for the research variables for all cases are depicted in Table 6.13 and 6.14.

Table 6.11

Design Framework with the Normal PLC

 

 

 

 

 

 

Factors Levels
Strategic 1_1 1_2 1_3
Choice
Initial
Technology A B C D
Choice

Table 6.12

Design Framework with the Reversed PLC

 

 

Factors Levels

Strategic 2-1 2-2 2—3 24
Choice

Initial

Technology A B C D
Choice

 

 

 

 

111

 

 

 

Table 6.6

 

Settings of the Research Variables

 

 

 

 

 

 

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113

6.1.7 Performance Measures

The optimal solution in the spreadsheet of Microsoft Excel® generates the total
objective function value, the dichotomized values of the terms in the objective fiinction,
and the values of control variables and state variables. The possible performance
measures comparing different scenarios include (1) optimal policy for the acquisition of
automation and flexibility, (2) optimal policy for the scrapping/reducing of conventional
capacity, (3) total automation capacity and the system's flexibility at time T, (4) total cost
performance, and (5) total flexibility performance.

The first and second performance measures might provide managers with insight

for the optimal investment policy in the presence of a specific corporate strategy. The

 

fourth and fifth performance measures might help corporate executives to understand how
they should plan their corporate strategies by bearing market demand behavior in their
minds. The third performance measure indicates how a firm's process technology evolves
in the product-technology box differently according to the choice of initial process

technology and corporate strategy.

 

 

114

6.2 RESULTS OF ANALYSIS

One interesting observation from the results of all the cases is that there is no

scrapping of existing or conventional capacity. Hence, the scrapping of conventional or

existing capacity will not be discussed.

6.2.1 Analysis with the Normal PLC

As described in Chapter V, there are two research factors: strategic choice and
initial process technology. For analysis with the Normal PLC, there are three levels of
strategic Choice —-1-1( mixed I ), 1-2 (cost efficiency), and 1-3 (flexibility) -- and four
levels of initial process technology -- A (high automation/low flexibility), B (high
automation/high flexibility), C (low automation/low flexibility), and D (low automation/

high flexibility). Hence, there are twelve cases in analysis with the Normal PLC.

6.2.1.1 Effect of Initial Process Technology

In this study a firm is assumed to Choose one of four different initial process
technologies, such as A (low automation/high flexibility), B (high automation/high
flBXibility), C (low automation/low flexibility), and D (high automation/low flexibility).
Table 6.15 depicts the optimal acquisition of automation capacity for twelve cases with
the Normal PLC, categorized according to the type of initial process technology. Figure
6.7 illustrates the results in Table 6.15 with graphs. Each graph depicts optimal policies
(patterns) for automation capacity acquisition corresponding to three different strategic
priorities for a given initial process technology. Since all four graphs look almost exactly

the same, it can be readily concluded that there is no effect of initial process technology on

the optimal acquisition policy for automation capacity.

115

Table 6.16 and Figure 6.8 illustrate the optimal acquisition of flexibility,
categorized according to the type of initial process technology. Again, there is no effect
of initial process technology on the optimal acquisition policy for flexibility. This is
because the objective of the strategic fit in the objective fiinction of the model has a
greater weight than the others. Table 6.17 and 6.18 display the cumulative automation
capacity and the cumulative system's flexibility. Figures 6.9 and 6.10 present the results in
Tables 6.17 and 6.18 with graphs. Since optimal acquisition policies for both automation
capacity and for flexibility with four different initial process technologies are the same
under a given strategic choice as depicted by Figures 6.7 and 6.8, the cumulative levels of
production capacity as depicted by four graphs in each figure are also identical. The upper
half of Table 6.31 displays the cost performance of cases with the Normal PLC. It is also
found that there is no effect of initial process technology on the overall cost performance.

No effect of initial process technology is caused by the highest weight being placed
on el and e2, the penalty cost for the strategic fit between an SBU's actual market
position and its goal for the market position in terms of volume and variety. For example,
suppose an SBU starts its operation with an initial process technology that has a high level
of automation capacity, and its corporate strategy is cost efficiency. The SBU's goal for
the volume demand growth, therefore, will be set at a high level and, in addition, the initial
market share is the same for all cases by the assumption. Hence, there should be the
acquisition of automation capacity to boost the market demand in volume in order to
minimize the excessive penalty of the strategic fit between actual market share and the
business goal in volume, even though there are other penalty costs incurred by the
acquisition of automation capacity.

Additional experiments with low values of el and e2 (10 and 20) show an effect of
initial process technology on the acquisition of flexibility (refer to Tables 6.32 - 6.35 and
Figures 6.25 - 6.29). According to Figure 6.31, Firm C and Firm D, which have a low

level of initial flexibility, invest in flexibility more than Firm A and Firm B, which have a

116

high level of initial flexibility. However, there is no difference in the acquisition of

automation. This is because the same initial production capacity, k(O), for all cases.

6.2.1.2 Effect of Strategic Choice

To analyze the effect of strategic choice, the results of cases with the Normal PLC
are categorized in terms of strategic Choice as depicted by Tables 19 through 22. The
results of these tables are graphically illustrated by Figures 6.11 through 6.14. Each graph
depicts optimal policies for flexibility acquisition according to the four different initial
process technologies within a strategic Choice. Since there is no effect of initial process
technology on the optimal acquisition policy, each graph in Figure 6.11 and 6.12 shows
almost a single line. The acquisition policy of automation capacity with strategic choice 1-
l in Figure 6.11 depicts a skewed investment during the mature stage because of the
strategic priority of cost efficiency pursued during that stage. The acquisition of
automation capacity with strategic choice 1-2 is substantial over the three stages, tailing
down toward the end of the mature stage. Since strategic choice 1-2 has the strategic
priority of cost efficiency throughout all three stages, a firm would try to purchase
automation capacity. This is partly because of the savings made from the economies of
scale, and partly because of the need to boost its SBU's market share in volume to meet
the SBU's goal for high growth in volume demand despite the relatively expensive
acquisition cost of automation capacity. This is why the acquisition of automation
capacity in the cases with strategic choice 1-2 is greater than the cases with the other two
strategic choices. The last graph illustrates the acquisition of automation capacity for the
cases with strategic Choice 1-3. Since the strategic priority for these cases is flexibility all
the time, there is no significant investment in automation capacity partly due to the low
growth goal for the market share in volume and partly due to a relatively high value for
the unit acquisition cost of automation capacity compared to the other cost coefficients in

the objective fiinction.

117

In Figure 6.12 the investment in flexibility is low for those cases with strategic
choice 1-2 as compared to the other cases. Since the strategic priority is cost efficiency
for all periods with strategic choice 1-2, a firm would not invest in flexibility due to the
relatively expensive unit acquisition cost of flexibility and a low priority given to the
objective of flexibility. The comparison between strategic choice 1-1 and strategic Choice
1-3 in Figure 6.12 indicates that the optimal acquisition policy for the flexibility of the two
cases is almost identical during the introductory stage where the total market demand in
variety increases sharply. Then, the acquisition of flexibility for the cases with strategic
choice 1-3 becomes greater than those with strategic choice 1—1 until the end of the time
horizon. The initial high investment in flexibility with these two strategic Choices is caused
by the high priority given to flexibility during the introductory stage and, consequently, the
relatively inexpensive unit acquisition cost of flexibility. The second reason is related to
the objective of minimizing the penalty cost of strategic fit by boosting the actual market
demand in variety, which meets the goal of high market share growth in variety pursued
during the introductory stage with both strategic Choices. The greater investment in
flexibility that strategic choice 1-3 shows compared to l-l after the introductory stage is
due to the difference in strategic priorities.

Figure 6.13 and Figure 6.14 depict the cumulative levels of automation capacity
and the system’s flexibility, respectively. In Figure 6.13 cumulative levels of automation
capacity for cases with strategic Choice 1-2 increase at a faster rate than the cases with the
other two strategic Choices reflecting the high investment with the choice 1-2 as depicted
by Figure 6.11. The cases with strategic choice 1-3 show the slowest growth of the
cumulative levels of automation capacity. However, in Figure 6.14 the cases with
strategic Choice 1-3 show a faster growth of the cumulative levels of the system's
flexibility than the cases with the other two strategic choices. Here, the cases with
strategic choice 1-2 show the slowest growth of the cumulative level of the system's

flexibility. Figure 6.13 and 6.14 show that the cases with strategic choice 1-1 have a

 

118

medium level of both automation capacity and system flexibility at the end of the mature
stage, compared to those cases with the other two Choices. The cases with strategic
Choice 1-2 have the highest level of automation capacity, but the lowest level of system's
flexibility at the end. The cases with strategic Choice 1-3 have the lowest level of
automation capacity, but the highest level of system's flexibility at the end.

As depicted by Table 6.31 there is again no effect of initial process technology on
the total cost performance. Among the four cases within each strategic choice, there is no
significant difference in the total cost performance. Among the three strategic choices,
strategic Choice 1-3 show the lowest total cost performance. Strategic choice 1-2 is the

worst in terms of total cost performance.

6.2.1.3 Conclusion

The results of the cases with the Normal PLC suggest that when the market
demand follows the pattern of the Normal PLC, the manufacturing firm pursuing a
strategy of cost efficiency should focus on investment in a rigid process technology with
high automation and low flexibility, whereas a firm pursuing a strategy of flexibility or
differentiation should focus on the investment for flexibility rather than the investment for
automation capacity. These results are managerially intuitive since a firm with the
strategic priority of cost efficiency wants to operate in the market with high volume and
low variety, and a firm with the strategic priority of flexibility wants to operate in the
market with low volume and high variety. The first firm should have a process technology
which allows it to achieve the economies of scale for price competition, and the latter one
should have a process technology which allows it to achieve the economies of scope for a
product-variety competition.

The low total cost performance for the cases with strategic choice 1-3 may be due
to their low investment in automation capacity. However, these cases have greater total

penalty costs for flexibility and strategic fit compared to the other cases with the Normal

 

119

PLC. The most cost effective performances for flexibility and strategic fit are given by the
cases with strategic choice 1-1, although they do have the highest total investment cost for
technology acquisition compared to the cases with the Normal PLC. Considering the
importance of the objectives of flexibility and strategic fit, the strategic Choice with the
Normal PLC should be 1-1 in which the strategic priority of a firm changes according to

external market conditions.

6.2.2 Analysis with the Reversed PLC

In the analysis with the Reversed PLC, the first three levels of strategic choice -- 2-
1 (mixed one), 2—2 (cost efficiency), and 2-3 (flexibility) -- are the same as the three
strategic Choices with the Normal PLC. The last choice, 2-4, is another mixed strategy in
which the strategic priority is changing from flexibility for the introductory stage, through
cost efficiency/flexibility for the growth stage, to flexibility for the mature stage. The
levels of initial process technology are A, B, C, and D, the same as for the analysis with

the Normal PLC. Therefore, the total number of cases with the Reversed PLC is sixteen.

6.2.2.1 Effect of Initial Process Technology

The results of the analysis with the Reversed PLC also indicate no effect of initial
process technology. Tables 6.23 and 6.24 present the acquisition of automation capacity
and flexibility with the Reversed PLC. The results are categorized according to the type
of initial process technology. These results are illustrated graphically in Figures 6.15 and
6.16. Tables 6.25 and 6.26 display the cumulative automation capacity and the system's
flexibility with the Reversed PLC. Figures 6.17 and 6.18 illustrate the results in the two
tables graphically. Again, all the graphs in each figure are almost identical. This is again

due to the high weight on the penalty cost (el and e2) for the strategic fit.

 

120

6.2.2.2 Effect of Strategic Choice

Tables 6.27 and 6.28 display the results for the optimal acquisition of automation
capacity and flexibility with the Reversed PLC, categorized in terms of strategic choice.
Figures 6.19 and 6.20 illustrate graphically the results in Tables 6.27 and 6.28,
respectively. In Figure 6.19 each of the results with strategic Choice 2-1 (Mixed Strategy)
and 2-2 (Cost Efficiency) shows almost the same pattern of optimal acquisition policy for
automation capacity as does the counterpart with the Normal PLC. With strategic choice
2-3 (Flexibility), there is some investment in automation capacity and significant
investment in flexibility during the reversed growth stage which is quite different from the
result for strategic Choice 1-3 with the Normal PLC as depicted by Figures 6.12 and 6.13.
This is mainly because of the continuing increase in market demand in terms of both
volume and variety during the reversed growth stage. The optimal acquisition policy for
automation capacity with strategic Choice 2-4 in Figure 6.19 shows a steady investment
over the three stages. However, the optimal acquisition for flexibility with strategic choice
2-4 in Figure 6.20 shows a significant amount of investment during the reversed growth
stage compared to the cases with strategic choices 2-1 and 2-2.

One interesting observation from both Figure 6.19 and Table 6.31 is that the cases
with strategic choice 2-4 have the lower total investment amount in automation capacity,
and yet they allow firms to achieve a slightly higher market share in volume compared to
those with strategic choice 2-3. In addition, with the strategic choice 2-4 firms can even
have a higher level of cumulative automation capacity at the end of the mature stage than
do those firms with strategic Choice 2-3. This is due to the difference in optimal
acquisition policies for automation capacity between the two strategic choices, depicted in
Figure 6.19. In Figure 6.19 strategic Choice 2-3 shows bulky investment during the
growth stage and the reversed grth stage, but strategic Choice 2—4 shows a steady and

incremental pattern of investment over the three stages. Because of the quadratic cost

 

121

function for the acquisition of automation capacity in the objective function, a bulky
investment costs more than a steady and incremental investment.

Tables 6.29 and 6.30 display cumulative automation capacity and flexibility,
respectively, and are categorized in terms of strategic choice. Figures 6.21 and 6.22
depict the results of the two tables. These graphs show that the cases with strategic
choice 2-1 generate process technology with medium levels of cumulative automation
capacity, and flexibility at the end. The cases with strategic choice 2-2 generate process
technology with the highest level of automation capacity and the lowest level of flexibility
at the end. The cases with strategic Choice 2-3 have the process technology with the
lowest level of automation capacity and the highest level of flexibility at the end. The
cases with strategic choice 2-4 have the process technology with a low but slightly higher
level of automation capacity than strategic choice 2-3, and a high but slightly lower level
of flexibility than strategic choice 2—3. This reflects the presence of cost
efficiency/flexibility priority with strategic choice 2-4 during the growth stage as compared
to flexibility priority with strategic choice 2-3 during that stage. During the growth stage
the cost efficiency/flexibility priority of strategic choice 2-4 encourages investment in
automation capacity, but deters the investment in flexibility a little more than the flexibility
priority of strategic choice 2-3. In Table 6.29 the cumulative automation capacities for the
cases of 2-3 are 888.5 and 988.5; those for the cases of 2-4 are a little higher, 946.0 and
1045.9. In Table 6.20 the cumulative system's flexibility for the cases of 2-3 are 470.0 and
381.0, and those for the cases of 2-4 are a little lower, 395.0 and 305.0

The bottom half of Table 6.31 depicts the cost performance of all sixteen cases
with the Reversed PLC. For total cost performance, the best strategic choice is 2-4,
which has a changing strategic priority congruent to the external market conditions. The
worst strategic Choice is 2-2 in which the strategic priority is cost efficiency for all three

stages.

 

122

6.2.2.3 Conclusion

Optimal investment policy for process technology for a firm operating in the
market with the Reversed PLC seems to be the same as when operating in the market with
the Normal PLC. Again, the results of analysis with Reversed PLC indicate that the
investment policy is high on the acquisition of automation capacity and low on the
acquisition of flexibility when the firin's strategic priority is cost efficiency. The results
also indicate that when the firm's strategic priority is flexibility a firm should invest more in
flexibility and less in automation capacity.

The best strategy with the Reversed PLC is 2-4. The reason why the choice 2-3,

flexibility, is inferior to 2-4 is that an SBU's goal of a relatively low growth in volume

 

demand discourages the investment in automation capacity during the growth stage, which
incurs an enormous penalty cost of the strategic fit later during the reversed growth stage.
Compared to strategic Choice 2-1, strategic choice 2-4 has relatively low total penalty
costs of strategic fit. This indicates that the firm's strategic priority has to be congruous
with the conditions of the external market environment. However, the cases with strategic
choice 2-4 have a higher total flexibility cost than those with strategic choice 2-1.
Considering the high unit penalty cost of flexibility for the cases with strategic choice 2-4,
one can see the difference is insignificant between the two choices.

The structure of process technology at the terminal time has the most important
strategic implication . Since the market following the pattern of the Reversed PLC will
have the continued growth of volume and variety in the future, the process technology
with high flexibility is key for survival in competition. Considering the importance of the
system's flexibility in the future, the best Choice would be 2-3. However, strategic choice
2-3 is considerably inferior in terms of total cost performance to strategic Choices 2-4.

In conclusion, the best strategic Choice with the Reversed PLC is strategic choice
2-4, which is a combination of strategic priorities: flexibility during the introductory stage,

cost efficiency/flexibility during the growth stage, and flexibility during the reversed

123

growth stage. Again, with the Reversed PLC the structure of initial process technology
has no effect on the overall performance and the optimal acquisition policy for process
technology when the strategic fit in the hierarchy of strategies is considered the most

important objective.

6.2.3 Comparison Between the Normal PLC and the Reversed PLC

This section is devoted to the discussion of the interaction effects of three research
factors -- the PLC, strategic choice, and initial process technology -- on the optimal
acquisition of new process technology, on the evolution of process technology, and on

overall cost performance.

6.2.3.1 Optimal Acquisition of New Process Technology

In Table 6.31 the investment for new process technology for each case with the
Reversed PLC is generally greater than the counterpart with the Normal PLC. This is due
to the continuing growth of both volume and variety demand in the Reversed PLC. For
example, the total investment cost with strategic Choice 2-3 is almost fifteen percent
higher than that with strategic choice 1-3. However, there is no significant difference in
the investment amount in both automation capacity and flexibility between strategic
choices 1-1 and 2-1, and between strategic choices 1—2 and 2-2.

There are no substitutions of new capacity for conventional capacity in any the
cases of with both the Normal PLC and the Reversed PLC. This is because the scrapping
cost outweighs the savings from the substitution with new process technology, which are
mainly the decrease in volume- or variety- related costs. Finally, strategic choice 2-4 with
the Reversed PLC has a slightly higher investment in automation capacity, but somewhat

lower investment in flexibility than strategic choice 1-3 with the Normal PLC.

 

124

6.2.3.2 Evolution of Process Technology

Figures 6.23 and 6.24 illustrate the evolution of process technology for the Normal
PLC and the Reversed PLC, respectively. The first point of each line is the initial position
of process technology. Each of the other points in each line represents the position of
process technology at the end of each stage of the PLC. There is no difference in the
pattern of evolution of process technology between the Normal PLC and the Reversed
PLC for each case. For cases with the first (1-1 and 2-1) and the second (1-2 and 2-2)
strategic Choices , there is little difference in the position between the Normal PLC and the
Reversed PLC. However, with the strategic choice of flexibility, such as 1-3 or 2-3, the
structure of process technology at the terminal time has higher levels of both automation
capacity and flexibility with the Reversed PLC than with the Normal PLC. With strategic
choice 2-4 the pattern of evolution of process technology shows a slightly higher level of
automation capacity, but a slightly lower level of flexibility for all three stages than with
strategic Choice 2-3.

Table 6.31 provides detailed information regarding the level of automation and
flexibility at the end of the mature stage for each case. In Table 6.31 the general salvage
value of process technology for each case with the Reversed PLC is higher than the
corresponding case with the Normal PLC. This reflects the higher amount of investment
in process technology for the cases with the Reversed PLC than for those with the Normal
PLC. One interesting observation is the higher level of the system's flexibility with
strategic Choice 2-4 compared to that with strategic Choice l-3, although the total
investment amount in flexibility with strategic choice 2-4 is lower than is with strategic
choice 1-3. This is again caused by the bulky investment in flexibility during the
introductory stage with strategic choice 1-3 shown in Figure 6.12, compared to the
steady investment with strategic choice 2-4 shown in Figure 6.20.

Table 6.31 shows that there is little difference in the cumulative automation

capacity and a system's flexibility at the end between strategic choices 1-1 and 2-1, and

 

125

between strategic choices 1-2 and 2-2. However, strategic Choice 2-3 has a terminal level
of cumulative automation capacity and system flexibility that is almost fifteen percent
higher and more than twenty percent higher, respectively, than strategic Choice 1-3.
Strategic Choice 2-4 has almost the same level of cumulative system flexibility as strategic
choice 1-3, but an almost twenty percent higher level of cumulative automation capacity
than strategic Choice 1—3 at the end. This high level of cumulative automation capacity at
the end is due to the combined effect of continuing growth of market demand in both
volume and variety with the Reversed PLC and the strategic priority of cost

efficiency/flexibility during the growth stage with strategic choice 2-4.

6.2.3.3 Cost Performance

Generally, the total cost performance of each of the first three strategic choices
with the Reversed PLC is inferior to the counterpart with the Normal PLC. The first two
strategic Choices, mixed I and cost efficiency, do not vary much in terms of the total cost
performance between the two types of PLC. However, the third strategic Choice of
flexibility with the Reversed PLC has a total cost much higher than does the one with the
Normal PLC. This higher cost is due to the considerable difference in the last four penalty

costs between the two types of PLC as depicted in Table 6.31.

6.3 SUMMARY OF FINDINGS

In this section the findings from the scenario analysis are summarized. The
findings related to the acquisition of new process technology are summarized in Section
6.3. 1, followed by those related to the evolution of process technology in Section 6.3.2,

and finally those related to the best strategic choice are summarized in Section 6.3.3

 

126

6.3.1 Acquisition of New Process Technology

1. According to the results of this study, a firm pursuing the strategic priority of
cost efficiency invests in new process technology more than does a firm pursuing flexibility
does

This may be due to the relatively small value for the unit acquisition cost for
flexibility compared to that for automation capacity. The results would be different if the
value for the unit acquisition of flexibility were significantly higher than the value in this
study. The cost of automation capacity is generally for hardware, such as tools, control
devices, and other automated equipment whereas the cost of flexibility is mostly for
software, such as control programs for a CNC machine or an FMS. Since the cost for
hardware is much higher than the cost for software, the values for the acquisition costs of
both automation capacity and flexibility used in this study seem to be fairly practical.
Therefore, it may be safe to conclude that the cost efficiency priority generally leads a firm
to a greater capital investment in new process technology than does the flexibility priority.

2. It is found that a firm whose strategic priority is flexibility invests more in the
system's flexibility than in automation capacity, as compared to low investment in
automation capacity and high investment in flexibility for a firm whose strategic priority is

cost efficiency.

6.3.2 Evolution of Process Technology

1. According to the results of this study, the size of investment in process
technology is larger with the Reversed PLC than with the Normal PLC.

2. There is little difference between the Normal PLC and the Reversed PLC in the
pattern of evolution of process technology when a firm's strategic Choice is changing

priorities (flexibility-cost efficiency/flexibility-cost efficiency) or cost efficiency.

 

127

3. With its strategic priority of flexibility a firm should invest more in both
automation capacity and flexibility when it Operates in a market with the Reversed PLC

than when operating with the Normal PLC.

6.3.3 Best Strategic Choice

1. According to the results of this study, for overall cost performance a firm with
cost efficiency priority is greatly inferior to the one with the flexibility priority in both
types of the PLC.

This is mainly due to the huge investment cost for automation capacity for the case
with the strategic priority of cost efficiency, although the total production cost with the
strategic priority of cost efficiency is lower than that with the priority of flexibility.

2. Based on the results of total cost performance, it can be concluded that a firm
operating in a market with the Normal PLC should choose a strategic priority of flexibility,
and that a firm operating in a market with the Reversed PLC should select its strategic
priority congruent to the conditions of the external market environment.

3. A firm can achieve the best performance for both volume and variety
flexibilities when it selects its strategic priority congruent to the external market
environment. The firm can achieve both high volume and high variety flexibilities with the
strategic choice of 1-1 when operating with the Normal PLC and with the strategic choice
of 2-4 when operating with the Reversed PLC.

4. It is found that the acquisition policy of new process technology is really a
strategic decision. A decision making process for technology acquisition should consider

the strategic fit in the hierarchy of strategies.

6.3.4 Conclusion
Due to the enormous computational time for each case, the parameters and

variables other than the experimental factors have fixed values for the scenario analysis in

 

128

this study. It may be difficult to generalize the results of scenario analysis unless there is
sufficient number of experiments with different settings of those parameters and variables.
However, based on the findings from the scenario analysis in this study, it is found that a
firm's corporate and/or business strategy is a key factor affecting an optimal acquisition
policy for process technology. The initial positioning of process technology by a firm is
found to affect the optimal acquisition policy only when a firm considers the strategic
priority as an insignificant factor for the technology acquisition decision. The external
market environment also affects the optimal acquisition policy. Particularly when a firm's
strategic priority is flexibility, the eflect of external market conditions on both total cost
performance and on an Optimal acquisition policy for new process technology becomes
significant.

In conclusion, the results of this scenario analysis demonstrate the importance of
the technology acquisition decision in fitting the corporate or business strategy to external
market conditions. For the optimal acquisition of new process technology, a firm has to
select its strategic priority congruent to external market conditions. Finally, the findings
from the scenario analysis in this dissertation support the argument by Wickham Skinner

that the technology decision has to made strategically.

 

129

 

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CHAPTER VII
SUMMARY AND CONCLUSIONS

This chapter summarizes the research conducted in this dissertation. Section 7.1
provides a brief overview of the content, focus, and findings of this research. Section 7.2
summarizes the contributions of this study. Finally, the possible fiiture extensions of this

research are suggested in Section 7.3.

7.1 RESEARCH OVERVIEW

This section summarizes the content and findings of this research. The sc0pe and
issues of this study are presented first, followed by the research model, research

methodology, and, finally, a summary of the findings.

7.1.1 Research Scope and Issues

The research in this dissertation is focused on the optimal acquisition policy within
the context of manufacturing strategy. The literature review in Chapter 11 suggests that,
first, the decision process of process technology acquisition should consider two important
factors: the objectives of various strategies in the hierarchy of strategy and the conditions
of external market environment. Secondly, it is necessary to modify the product-process
matrix theory so that it emphasizes the importance of flexibility in a fiercely competitive
world of advanced technology. The research scope of this dissertation is to develop a
conceptual framework for understanding the strategic implications of the effect of the two
factors on the optimal acquisition of process technology within the context of the PLC
theory, and to explore research issues analytically.

Two key research issues are identified in this study. The first issue is the effect of

two major research factors, strategic priority and external market environment, on the
173

 

 

174

optimal acquisition of new process technology. The second issue is the effect of the two

factors on the evolution of process technology throughout the various stages of the PLC.

7.1.2 Research Model

The research model developed in this dissertation is a dynamic optimal control
model. It has an objective function minimizing the total cost, which includes the
acquisition cost of technology, production costs, penalty costs for volume and variety
flexibilities, and penalty costs for the strategic fit between an SBU's actual market position
and its goal in terms of both volume and variety demand. The model has five control

variables and seven state variables.

 

7.1.3 Research Methodology

A design for scenario analysis is developed using three research factors: (1) PLC
type, (2) strategic choice (priority), and (3) the structure of initial process technology.
The design has twenty-eight replications. Since the closed form solution of the problem
can not be obtained, the model is converted to a discrete-time, non-linear model. The
converted model is solved by using the GRG2 algorithm in a spreadsheet software,

Microsoft Excel®.

7.1.4 Summary of Findings

Based on the discussions of the results of the scenario analysis, findings can be
summarized as follows:

1. It is found that a firm whose strategic priority is flexibility invests high in the
system’s flexibility and low in automation capacity, as compared to low investment in

automation capacity and high investment in flexibility for a firm whose strategic priority is

 

cost efficiency.

 

175

2. According to the result of this study, the size of investment in process
technology is larger with the Reversed PLC than with the Normal PLC.

3. There is little difference in the evolution of process technology between the
Normal PLC and the Reversed PLC when a firm’s strategic choice is a mixed one
(flexibility-cost efiiciency/flexibility-cost efficiency) or cost efficiency.

4. According to the result of this study, for total cost performance a firm with a
priority of cost efficiency is greatly inferior to the one with a priority of flexibility. This
holds for both types of the PLC.

5. For the optimal acquisition of new process technology in terms of flexibility and
strategic fit, a firm should select its strategic priority congruent to external market
conditions.

6. The acquisition policy of new process technology is found as a strategic
decision. In a decision making process for technology acquisition, it is important to align
the objectives of various strategies in the hierarchy of strategies with the external market
environment. This alignment will optimize the long-term performance of a manufacturing
system through the acquisition of new process technology.

Based on the findings in this study, it may be difficult to make generalizations from
the preceding conclusions which will apply to problems in the real world. However, this
study is expected to lay a cornerstone for fiiture studies in this area. It is hoped that the

future studies will further enrich the model to capture the dynamics of strategic variables.

7.1.5 Limitations of Study
This study has some limitations as follows:
1. The results of scenario analysis may be approximated solutions due to the large

size of time step in the state equations.

 

176

2. The capacity of solution algorithm is not suflicient to allow more than 20 time
periods for the PLC. GRG2 in Microsofi Excel® can handle a non-linear problem of up
to 200 variables and 150 constraints.

3. Parameters are not industry-verified.

4. Each solution in scenario analysis may be a local optimum. There is no way to
verify whether each solution is a global optimum or not.

5. The solutions may be sensitive to the initial values of some state variables.

7.2 CONTRIBUTIONS

The contributions of this study can be viewed in terms of three aspects: (1)
research methodology for the study of manufacturing strategy, (2) the content of research,
and (3) managerial implication of research findings. First, this study broadened the
knowledge base in terms of research methodology for future research in the area of
business strategy. In particular, there are few analytic studies for problems related to
manufacturing strategy. Because of the dynamic interaction among variables in a strategic
problem, it is indispensable to develop dynamic control models such as the one in this
dissertation, in order to facilitate both academicians and practitioners understanding of the
problem structure.

Second, this dissertation suggests a new conceptual framework for the study of
technology acquisition. This study broadly views the problem of technology acquisition
by relating the optimal acquisition policy to some strategic and environmental variables.
The conceptual frameworks for the decision process of process technology acquisition and
for the structure of new process technology suggested by this study are expected to guide
future studies in the area of manufacturing strategy.

Finally, the findings in this study can help managers to make decisions for the

acquisition of new process technology in a strategic fashion. Analytic studies for process

177

technology acquisition in the past generally dealt with narrowly defined problems and
myopically analyzed the problems without due consideration of the strategic implications
of technology acquisition decisions. This study provides managers with a direction for
acquisition decisions of new process technology which will lead to the best alignment of

various strategies in the hierarchy of strategies.

7 .3 POSSIBLE RESEARCH EXTENSIONS

The findings in this study suggests the following research extensions in the future.

1) There should be more efforts to find an algorithm to solve a problem of
reasonable size. Because of the large number of variables and constraints contained in the
model the solution time and the accuracy of solution of an algorithm become critical for
future extensions of this study. In fact, if each period is divided into several steps in order
to attain more accurate and smooth solutions, the efficiency of the solution algorithm
becomes deteriorated. An efficient algorithm will also allow an investigation of the effect
of a longer planning horizon on the technology acquisition policy.

2) A sensitivity analysis with some key parameters is a possible research extension
in the future. In particular, the level of initial capacity for volume and variety can be
changed to analyze in more detail the effect of initial process technology. The penalty
costs for the strategic fit, el and e2, can also be included in a sensitivity analysis. From
the results with several different levels of these two penalty costs, one can find how the
difference in the relative importance, perceived by decision makers, between the functional
strategy and the corporate and/or business strategy is affecting the optimal acquisition of
process technology.

3) The model can be extended to a game theoretic one. In this study, the model
considers a single firm case. With an extension of Duopoly or Oligopoly the game

theoretic aspect of technology acquisition problem can be analyzed. In particular, it would

 

178

be interesting to study the interaction effect of strategic choices between two competitors
or among several competitors on the optimal technology acquisition decision of a firm.

4) Finally, the parameterization of the model should be in the list of possible fiiture
research extensions. Once the parameters of this model is estimated with empirical data, it
can be used to evaluate strategically the optimal acquisition problem for new process
technology of a firm. The parameterization would be possible when real data become

available from industry after long experience of advanced process technology,

 

 

APPENDICES

 

APPENDIX I

179

Derivation of Necessary Conditions

The first-order derivatives of adjoint variables are like the followings;
M(t) = - 6W5v = M(t)[7ria(t)-7t2] - rzcirpm-vrt» - 2e1<vrt> -vrt»]e-rt, (al-u

M(t) = - 5H/5m = 7&2(t)[¢1f(t) - 492] - [205(Q(t) - m(t)) - 2620100) - M(t))le'“, (211-2)

l3'(t) = - 6H/5b1 = K3(t)0ta(t) + p(t)e-rt, (a1-3)
M0) = - 5W5b2 = ' M(t)l35'1(t)(91f(t) - sz(t)) + p(t)“, (211-4)
X5'(t) = — 5H/5k = 0, (al-S)
K6'(t) = - arr/5x = o, (a1-6)

M(t) = - arr/6s = M(tmbzromrfrt) - pza(t))5'2(t)- (a1-7)

APPENDIX II

180

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