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Supply Chain Flexibility aspects and their
impact on customer satisfaction
Open University the Netherlands
Faculty of Management
Master of Supply Chain Management
J.Manders BEng (850071988)
Supervisor: Dr. P. W. Th. Ghijsen
Second supervisor: Prof. Dr. J. Semeijn
Date: July 2009
Page 1 of 91
Summary
Purpose
Nowadays firms face a complex, continuously changing and uncertain environment through trends
and changes in the area of globalization, technological changes and innovations and changes in the
customer’s needs and expectations. To cope with this increasingly uncertain and quickly changing
environment firms strive for flexibility. In the existing literature there are a lot of different definitions
of flexibility (Sethi and Sethi 1990; De Toni and Tonchia 1998; Vickery et al. 1999; Vokurka and
O'Leary-Kelly 2000; Lummus et al. 2003), however there is no uniform concept that is broadly
accepted and most of the literature on flexibility focuses on manufacturing flexibility. To achieve the
level of flexibility that adds value to the customers, firms must look beyond the manufacturing
flexibility, namely flexibility from a supply chain- or value chain perspective. This research proposes
to determine the impact of supply chain flexibility on customer satisfaction using a comprehensive
model for supply chain flexibility.
Methodology
An overview of the flexibility theory is presented, describing the elements of flexibility, the
perspectives on flexibility, the dimensions of flexibility, the different aspects of flexibility and the
comparison of flexibility and agility. A definition of flexibility and supply chain flexibility is stated
and to determine which supply chain flexibilities are important in relation to customer satisfaction,
seven customer facing capabilities are selected and admitted in a model that can serve as a testable
framework for relating supply chain flexibility to customer satisfaction. These seven flexibility
capabilities are product modification- and new product flexibility as part of product development
flexibility, mix- and volume flexibility as part of manufacturing flexibility, physical distribution- and
demand management flexibility as part of logistics flexibility and strategy development flexibility as
part of spanning flexibility. A survey based, empirical, cross sectional study is used to collect data
from manufacturing firms in the Netherlands with one hundred or more employees. Descriptive
statistics, correlation- and structural equations modeling are used to test the results.
Results
The data for this research are collected under 1000 manufacturing firms in the Netherlands with 100
or more employees. Out of 100 responses received (3 undeliverable, 8 blank returns and 6
incomplete), 83 were usable resulting in a response rate of 8,3 %. The statistical results indicate that
there is a positive relationship between the flexibility capabilities product modification-, mix-,
volume-, physical distribution-, demand management- and strategy development flexibility and
customer satisfaction when tested one at the time.
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When testing the comprehensive model using path analysis only hypothesis 1, 5 and 7 are supported.
Thus only product modification flexibility, physical distribution flexibility and strategy development
flexibility have a significant positive impact on customer satisfaction.
Discussion
The results of the one tot one analysis of the flexibility capability dimensions for product
modification, volume, mix, physical distribution, demand management and strategy development in
relation with customer satisfaction are comparable with the results of previous studies from Zhang et
al. (2002a; 2003; 2005; 2006). As there is no previous research on this topic in which a
comprehensive model with all flexibility capability dimensions in relation to customer satisfaction are
tested at once, no comparisons with model based results can be made using previous literature.
Previous literature indicates that the results from this study testing the comprehensive model can
possibly be explained by the visibility of the product modification-, physical distribution and strategy
development flexibility for the customer and the importance of these flexibilities from a mass
customization point of view.
Conclusion
Flexibility covers many related aspects. A broad perspective is needed to determine which flexibility
is required in a particular situation using a particular strategy to reach a predetermined goal.
Thus flexibility must not be tested solely, but in a model that can serve as a testable framework for
that situation. To answer the question what the impact is of supply chain flexibility on customer
satisfaction, the results show that only product modification flexibility-, physical distribution
flexibility and strategy development flexibility have a significant positive impact on customer
satisfaction (see also the table below).
Relationship Coefficient t-value p-value Conclusion R²
Product Modification Flexibility => Customer Satisfaction 0,208 2,543 0,0064 H1 supported 0,478098
New Product Flexibility => Customer Satisfaction -0,139 1,406 0,0817 H2 not supported
Volume Flexibility => Customer Satisfaction 0,108 1,121 0,1328 H3 not supported
Mix Flexibility => Customer Satisfaction 0,052 0,573 0,2840 H4 not supported
Physical Distribution Flexibility => Customer Satisfaction 0,200 2,741 0,0038 H5 supported
Demand Management Flexibility => Customer Satisfaction 0,105 0,925 0,1789 H6 not supported
Strategy Development Flexibility => Customer Satisfaction 0,408 3,572 0,0003 H7 supported
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Managerial- and theoretical implications
The outcomes of this study reveal that flexibility must be considered in a broad perspective in relation
to the situation, the strategy and the goal that must be obtained. Therefore a model based approach
should be used where all flexibility dimensions that are important for that situation can be compared
together instead of looking to all flexibility dimensions separately, which is also not the case in
practice where different flexibility dimensions participate together. In answering the research question
“what the impact is of supply chain flexibility on customer satisfaction”, only the dimensions of
flexibility that are important from the customer point of view (the customer facing or customer
pleasing capabilities) are part of the investigation. In the same way managers need to understand that
depending on their situation and their own firm’s relationship with the entire supply chain they must
strive for the right selection of flexibility dimensions to make the right consideration to reach the
predetermined results. Depending on the situation not only the customer facing capability dimensions
can be important, but also the supporting flexibility competences. Based on the results of this research
to improve customer satisfaction the flexibility capabilities product modification flexibility, physical
distribution flexibility and strategy development flexibility are important and should therefore be
stimulated in strive for higher customer satisfaction levels.
Future research
The research is carried out under 1000 manufacturing firms in the Netherlands with one hundred or
more employees using questionnaires filled in through managing general-, logistics- or manufacturing
representatives. To prevent the possibility of potential bias future research can be done to test this
model using a survey investigation under manufacturing firms and its customers at the same time or
repeat this test under the customers of manufacturing firms. Future research can also be accomplished
taking the supply chain instead of the firm as the unit of analyses or repeat this study for small and
medium sized firms or in one more different countries to retest the results. Another option for future
research is to use a longitudinal study to determine how the relation between supply chain flexibility
and customer satisfaction develops over time. At last future research can be accomplished to test if
there is a relationship between the different flexibility dimensions and expand the model with the
internal competences or different aspects that could be important.
Value
This is (one of) the first empirical studies in which a comprehensive model of supply chain flexibility
is tested in practice, based on a broad perspective on flexibility.
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Acknowledgements
Almost at the end of my Master study of Supply Chain Management at the Open University it is time
to write my acknowledgements. Looking at the process of choosing a subject, the execution of the
research, writing this thesis, the escalating time schemes, the difficulties, the drive to know exactly
what it means, get to know more and more about this subject and ending with this report as the final
result, I am a satisfied follower of “the thesis myth”. Below I would like to thank the people who
supported me:
• Paul Ghijsen for the excellent support with respect to the content and for guiding me through this
process by asking the right questions.
• Janjaap Semeijn for his very useful hints regarding the questionnaire and collecting information.
• All the students from the graduate meetings telling the experiences they had in writing the thesis
and the graduating process.
• Marloes van Rooij for checking my English writing.
• My colleagues from Fontys ILEC for the interesting conversations about this subject and for the
support during the whole master study.
• My employer Fontys for giving me the opportunity to use the response number and the employees
of Fontys for supporting me. Specially the employees of library who supported me with help in
gathering the right information and again and again the prolongation of the books, Dave from the
copy shop who made 1000 questionnaires in book form right on time and the caretakers for
stamping 1000 envelopes with questionnaires and bundling the returned questionnaires.
• All the people from the manufacturing companies who filled in my questionnaire and helped me
to finalize this research and my study.
• My dad for helping me with a lot of time consuming activities such as searching all the website-
and email addresses on the internet, labeling 2000 envelopes and partly delivering the wine
bottles to the selected respondents.
Finally I would like to thank my friends and family, in particular Lea, Frans and Vreni for all the
patience and support they have given to me studying at the Open University and especially in the last
stressful period writing my thesis.
Jorieke Manders
Mariahout, 14 July 2009
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Table of contents
1. INTRODUCTION..........................................................................................................................7
2. THEORETICAL BACKGROUND............................................................................................10
2.1 HISTORICAL DEVELOPMENT OF FLEXIBILITY ..........................................................................10
2.2 FLEXIBILITY ............................................................................................................................10
2.2.1 Elements of flexibility ......................................................................................................11
2.2.2 Perspectives on flexibility................................................................................................12
2.2.3 Dimensions of flexibility ..................................................................................................15
2.2.4 Different aspects of flexibility..........................................................................................18
2.2.5 Flexibility versus agility ..................................................................................................19
2.3 SUPPLY CHAIN FLEXIBILITY ...................................................................................................20
2.4 CONCEPTUAL MODEL ..............................................................................................................21
2.4.1 Dimensions of supply chain flexibility.............................................................................23
2.4.2 Customer satisfaction ......................................................................................................26
3. HYPOTHESES AND RESEARCH MODEL............................................................................28
3.1 HYPOTHESES ...........................................................................................................................28
3.1.1 Product development flexibility capabilities and customer satisfaction .........................29
3.1.2 Manufacturing flexibility capabilities and customer satisfaction....................................29
3.1.3 Logistics flexibility capabilities and customer satisfaction .............................................30
3.1.4 Spanning flexibility capability and customer satisfaction ...............................................30
3.2 RESEARCH MODEL ..................................................................................................................31
4. METHODOLOGY.......................................................................................................................33
4.1 THEORETICAL FOUNDATION...................................................................................................33
4.2 RESEARCH DESIGN .................................................................................................................34
4.3 DATA COLLECTION METHOD..................................................................................................35
4.4 IMPLEMENTATION ...................................................................................................................35
4.4.1 Sample .............................................................................................................................35
4.4.2 Instruments ......................................................................................................................36
4.4.3 Response rates .................................................................................................................37
5. RESULTS .....................................................................................................................................38
5.1 RELIABILITY ANALYSES..........................................................................................................38
5.2 HYPOTHESIS ANALYSES ..........................................................................................................41
5.2.1 Correlation analyses .......................................................................................................41
5.2.2 Smart PLS........................................................................................................................42
5.2.3 Results..............................................................................................................................43
6. DISCUSSION, CONCLUSION, LIMITATIONS AND FURTHER RESEARCH ...............44
6.1 DISCUSSION.............................................................................................................................44
6.2 CONCLUSION AND IMPLICATIONS ...........................................................................................46
6.2.1 Managerial implications..................................................................................................47
6.2.2 Theoretical implications..................................................................................................48
6.3 LIMITATIONS AND FURTHER RESEARCH .................................................................................49
LITERATURE ....................................................................................................................................51
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APPENDIX 1. OVERVIEW DIMENSIONS OF MANUFACTURING FLEXIBILITY.........60
APPENDIX 2. OVERVIEW DIMENSIONS OF SUPPLY CHAIN FLEXIBILITY................64
APPENDIX 3. THE CONCEPTUAL MODEL OF SUPPLY CHAIN FLEXIBILITY ...........66
APPENDIX 4. RESEARCH MODEL...........................................................................................67
APPENDIX 5. OVERVIEW OF SIC CODES IN SAMPLE.......................................................68
APPENDIX 6. QUESTIONS IN SURVEY ...................................................................................69
APPENDIX 7. OVERVIEW RESPONDENTS OTHER POSITIONS ......................................71
APPENDIX 8. OVERVIEW RELIABILITY ANALYSES.........................................................72
APPENDIX 9. HISTOGRAMS FLEXIBILITY CAPABILITIES AND CUSTOMER
SATISFACTION...................................................................................................80
APPENDIX 10. CORRELATION EACH FLEXIBILITY CAPABILITY DIMENSION
AND CUSTOMER SATISFACTION ..................................................................84
APPENDIX 11. RESEARCHMODEL TESTED USING SMART PLS .......................................91
Figures and tables
Figure 1: Visualised problem statement ..................................................................................................9
Figure 2: Hierarchy of flexibility dimensions (Koste and Malhotra, 1999)..........................................18
Figure 3: Conceptual model Supply Chain Flexibility..........................................................................22
Figure 4: Research model......................................................................................................................31
Figure 5: Research model tested using Smart PLS ...............................................................................42
Table 1: Dimensions of manufacturing flexibility ................................................................................17
Table 2: Dimensions of supply chain flexibility ...................................................................................17
Table 4: Overview reliability analysis of each construct of flexibility and customer satisfaction........38
Table 5: Descriptive statistics on item level..........................................................................................39
Table 6: Descriptive statistics on factor level .......................................................................................40
Table 7: Correlation matrix all dimensions research model..................................................................41
Table 8: Results structural model..........................................................................................................43
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1. Introduction
Nowadays firms face a complex, continuously changing and uncertain environment through trends
and changes in the area of globalization, technological changes and innovations and changes in the
customer’s needs and expectations (Huber 1984; Jaikumar 1986; Doll and Vonderembse 1991;
Parnell et al. 2000; Germain et al. 2001; Duclos et al. 2003; Pujawan 2004; Skintzi 2007; Tachizawa
and Thomsen 2007):
• Globalization. Through the increasing communication opportunities and the break-through of the
traditional trade- and production-barriers firms can execute their activities at the most profitable
locations all around the world. This leads to worldwide competition and a focus on core
competencies resulting in the outsourcing of none core activities (Gattorna 1998; Skjoett-Larsen
2000; Schary and Skjoett-Larsen 2001; Christopher 2005).
• Technological changes and innovations. Technological developments in the area of ICT,
including internet applications, e-business and e-commerce make the information exchange
between firms as well as firms and their customers possible. These technological innovations
increase the globalization and the worldwide time-based competition because, with regard to the
exchange of information, distances and time are no longer an issue (Keil et al. 2001; Yen 2002;
Boyson et al. 2003; Ghiassi and Spera 2003; Lancioni et al. 2003; Gunasekaran and Ngai 2004)
• Changes in customer’s needs and expectations. Today’s customer is smart and clever and
demands more and more in terms of delivery time, quality, availability, reliability, product
diversity and service (Pujawan 2004; Kumar and Deshmukh 2006).The needs of these customers
can radically change in every moment, even when the product is in development (Cooper 2000;
MacCormack et al. 2001).The trend towards “mass customization” challenges firms to deliver a
individually customized product to the same conditions as a mass produced one (Hart 1995;
Gilmore and Pine 1997; Kelly and Roozenboom 1999). Beyond this trend to mass customization
the product life cycles become shorter and the product proliferation grows (Vokurka and Fliedner
1998a). As a result of these developments the competitiveness of firms will increasingly depend
on the ability to deliver individually customized products rapidly and right on time (Skjoett-
Larsen 2000; Christopher 2005).
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To cope with this increasingly uncertain and quickly changing environment firms aim for flexibility
in their organization (Gerwin 1987; Sethi and Sethi 1990; Fawcett et al. 1996; Malhotra et al. 1996;
Volberda 1997; Ward et al. 1998; Gunasekaran 1999; Grewal and Tansuhaj 2001; Stevenson and
Spring 2007; Chandra and Grabis 2009; Saleh et al. 2009). So flexibility will be seen as a reaction on
environmental uncertainty (Swamidass and Newell 1987; De Meyer et al. 1989; Suarez et al. 1991;
Gerwin 1993; Upton 1994; Bertrand 2003; Tachizawa and Thomsen 2007; Hallgren and Olhager
2009) and as important requirement to survive (Skintzi 2007), because it helps a firm to build a
competitive advantage (Ettlie and Penner-Hahn 1994; Sanchez 1995; Upton 1997; Chang et al. 2003;
Garavelli 2003; Dreyer and Grønhaug 2004; Pujawan 2004; Phillips and Wright 2009).
There are a lot of different definitions of flexibility, but there is no uniform concept that is broadly
accepted. The current literature on flexibility mainly focuses on manufacturing flexibility (Upton
1994; Koste and Malhotra 1999; Vokurka and O'Leary-Kelly 2000; Jack and Raturi 2002; Koste et al.
2004). To achieve the level of flexibility that adds value to the customers firms must look beyond the
manufacturing flexibility, namely flexibility from a supply chain- or value chain perspective (Day
1994; Eloranta et al. 1995; Krajewski et al. 2005; Schmenner and Tatikonda 2005; Slack 2005b)
Supply chain flexibility should be broadly defined with components of flexibility on the intrafirm
level (from the internal perspective) and on the interfirm level (from the external perspective)
(Vickery et al. 1999; Slack 2005b; Stevenson and Spring 2007) and includes product development-,
production-, logistics and spanning flexibility (Day 1994; Zhang et al. 2002b).
The literature lacks a comprehensive model for supply chain flexibility that is tested. In this study a
model of supply chain flexibility based on Zhang et al. (2002b; 2002a; 2003; 2005; 2006) will be
developed and tested from the perspective of managers from manufacturing firms in the Netherlands.
The problem statement of this study is described in the following research question:
“What is the impact of supply chain flexibility on customer satisfaction?”
This problem statement is visualized in figure 1 using a supply chain as starting point.
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Figure 1: Visualized problem statement
In figure 1 the processes and the goods- and information flow in a supply chain, which are needed to
foresee in customer’s demands, needs or expectations, are visualized. The question central in this
study is of more or less flexibility in these processes (including the goods- and information flows and
the organizational structure and strategies behind them) lead to an increased or decreased level of
customer satisfaction.
The problem statement can be divided in a number of sub questions:
1. What is flexibility?
2. What is supply chain flexibility?
3. What are the dimensions of supply chain flexibility?
4. What are the “competences” and “capabilities” of (the dimensions of) supply chain flexibility?
5. What is customer satisfaction?
6. Which relationship exists between the degree of supply chain flexibility and customer
satisfaction? In other words: have the different dimensions of flexibility a positive or negative
effect on customer satisfaction?
In the following chapter the theoretical background and the conceptual model will be described.
The hypotheses derived from literature and the research model are discussed in chapter 3. The
research methodology is presented in chapter 4. In chapter 5 the results will be analyzed and worked
out on the basis of the formulated hypothesis. The findings will be discussed in a wider perspective in
chapter 6 discussion and conclusion, including the implications, limitations and the recommendations
for further research.
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2. Theoretical background
This chapter provides an overview of the literature on flexibility and throughout this chapter the
research sub questions one to five will be answered from a theoretical perspective.
First the historical development of flexibility will be described. In paragraph 2.2 flexibility will be
reviewed in view of the elements of flexibility, the perspectives on flexibility, the dimensions of
flexibility and different aspects of flexibility. At the end of paragraph 2.2 a definition of flexibility
will be given. Supply chain flexibility, the subject of this research, will be described in paragraph 2.3.
In paragraph 2.4 the conceptual model will be presented, including the definition of the different
aspects of supply chain flexibility and the aspect customer service.
2.1 Historical development of flexibility
The word flexibility has been first introduced into the economics literature by Stigler in the 1930’s, in
the context of a firm’s ability to accommodate to greater variations in the demand output (Carlsson
1989; De Toni and Tonchia 1998, 2005). Later all forms of turbulence in the firm’s environment
become important and with the occurrence of the information technology in the 1980’s the research
on flexibility increased (Wadhwa and Rao 2003). The research in the 1980’s and 1990’s mainly
focused on flexibility from a manufacturing perspective and led to the development of conceptual
frameworks, models and measures for manufacturing flexibility (Bertrand 2003; Wadhwa and Rao
2003; Kumar et al. 2006; Avittathur and Swamidass 2007; Stevenson and Spring 2007). In the last
years the focus is also shifting to the flexibility of organizations and supply chains (Koste and
Malhotra 1999; Bertrand 2003; Duclos et al. 2003; Wadhwa and Rao 2003; Avittathur and Swamidass
2007; Stevenson and Spring 2007).
2.2 Flexibility
Flexibility is a complex and multidimensional concept (Sethi and Sethi 1990; Upton 1994, 1995;
Garavelli 2003) and it has been very difficult to define flexibility satisfactorily (Dreyer and Grønhaug
2004). Referring to the different papers flexibility can be analyzed in terms of:
• The elements of flexibility;
• The perspectives on flexibility;
• The dimensions of flexibility;
• Different aspects of flexibility.
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2.2.1 Elements of flexibility
Flexibility has three distinctive elements: range, mobility (response) and uniformity (Sethi and Sethi
1990; Upton 1995; Reichart 2007). De Leeuw and Volberda (1996) describe these elements as
variety, rapidity and procedures.
• Range is the different states a system can achieve (Sethi and Sethi 1990; Reichart 2007). Slack
(1983) and Upton (1994) equated the range to the number of different positions of flexible
options, that can be achieved for a give flexibility dimension (Koste and Malhotra 1999; Slack
2005a). However, Upton (1994) also referred to a different aspect of range, namely the effect of
differentiation between the flexible options. In order to limit confusion Koste and Malhotra
(1999) used the terminologies range-number and range-heterogeneity.
Range-number is the number of viable or possible options which a system or resource can
achieve. The range-number element represents a strict numeral count of the flexible options
(Koste and Malhotra 1999, 2000). Range-heterogeneity captures the differences between the
options (Koste and Malhotra 1999). Range increases with the size of a set of options or
alternatives, which may be accommodated or affected. Examples are the range of sizes of
components and products that can be processed and the range of volume of output for which a
plant is profitable (Upton 1994; Bertrand 2003).
• Mobility is the ease with which an organization can change from one state, for instance making
one product, to another (Upton 1994; Koste and Malhotra 1999). It refers to transition penalties
from moving with the range (Upton 1994) and the “ease of movement”(Slack 2005a). Low values
of transition penalties imply high mobility (Upton 1994). Mobility may be measured by time or
costs of change (Upton 1994). For instance the mobility required for a product line can be
measured by the set-up times and set-up costs required for changing between product types, and
the mobility of output volume of plant can be measured by the cost and time it takes to change the
output volume from one level tot the other within the range (Bertrand 2003).
• Uniformity is the ability of the firm to maintain performance standards as it switches among
products and captures the similarity of these performance outcomes within the range (Koste and
Malhotra 1999; Bertrand 2003; Zhang et al. 2003). Uniformity refers to the extent to which
general performance measures such as efficiency, productivity, product quality and production or
processing times and costs are indifferent to which particular point the system operates (Koste and
Malhotra 1999; Bertrand 2003).
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High uniformity would imply the ability to maintain quality as the product changes (Sethi and
Sethi 1990; Upton 1995; De Leeuw and Volberda 1996). For instance a production line that can
produce each of the products within the range at the same costs per unit, is viewed as more
flexible than a line that can produce the same product range, at the same average costs per
product, but some products are produced at lower than average costs and some are produced at
higher than average costs. (Gerwin 1993; Koste and Malhotra 1999; Zhang et al. 2002a; Bertrand
2003; Sánchez and Pérez Pérez 2005; Reichart 2007).
2.2.2 Perspectives on flexibility
Flexibility can be viewed within three perspectives, the economic perspective, the organizational
perspective or approach and the manufacturing (management)- or operational perspective.
2.2.2.1 Economic perspective
From an economic perspective, the range, mobility, and uniformity are explained as the stores of
many options, short-term response, and flat cost curve over a range of product volume. This
perspective is opened up by Stigler (1939) discussing flexibility in terms of a cost curve. He considers
a plant to be flexible if it has a relatively flat average cost curve. One of the first investigations on the
characteristics of flexibility was carried out by Marshak and Nelson (1962), who suggested three
alternative definitions of flexibility: entity of marginal costs; entity of the expected marginal profits (a
plant is more flexible if it makes greater profits in new market positions); and amplitude of the set of
choice (an initial position is more flexible if it permits higher number of positions in successive
periods). They argue that minimum average costs (the slope of the marginal cost curve) vary inversely
with flexibility (Sethi and Sethi 1990; Zhang et al. 2002b). They also claim the complement “the
greater the flexibility in decision making, the greater the value of information gathering”. Mills
(1984) takes these ideas one step further and shows the determination of endogenous flexibility in
competitive markets with demand fluctuations (Sethi and Sethi 1990).
Formalization of the notation of flexibility in a sequential decision context and relating the amount of
information the decision maker expects to receive have among others been attempted by Mandelbaum
and Buzacott (1990). He defines flexibility as “the ability to respond effectively to changing
circumstances” and observes that it can be characterized into action flexibility, “the capacity for
taking new actions to meet new circumstances”, and state flexibility, “the capacity to continue
functioning effectively despite changes in environment”.
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Klein (1984) distinguishes between static and dynamic efficiency and then divides the latter in two
classes of flexibility: one which deals with risk associated with repetitive events and the other with the
uncertainty of new events. States efficiency is the firm’s ability to combine the inputs in an optimal
way, while dynamic efficiency refers to its ability to steer towards new and profitable situations (De
Toni and Tonchia 2005).
Jones and Ostroy (1984) consider explicitly the cost of switching from one action in this period to
another in the next. They emphasize: “the way flexibility is used to exploit forthcoming information
may be dictated by attitudes toward risk; but flexible positions are attractive not because they are safe
stores of value, but because they are good stores of options”. They also indicate that there has been a
long tradition of isolated recognition that flexibility choice is a component of wide range of economic
decision and that the limited role of flexibility in economic theory is perhaps due to difficulties in
defining flexibility in a universal way and obtaining formal results without model-specific
qualifications.
In conclusion a great part of the studies on economic flexibility is limited to the consideration of
flexibility as the ability to respond only to fluctuations in demand (Sethi and Sethi 1990; Day 1994;
Zhang et al. 2002b; De Toni and Tonchia 2005; Zhang et al. 2009).
2.2.2.2 Organizational perspective
From the organizational perspective, flexibility is the firm’s ability to suffer limited change without
serious disorganization (Sethi and Sethi 1990; Zhang et al. 2002b; Zhang et al. 2009). March and
Simon (1958) have introduced the concept of organizational slack, which provides an organization
with excess resources to cope with internal as well as some external uncertainties.
At the macro-organizational level, description of models of organizations that are flexible enough to
operate responsively in a rapidly changing environment are contributed by Burns and Stalker’s (1961)
organic structure (as opposite to the mechanistic structure), Emery and Frist’s (1962) sociotechnical
system, Child (1972) matrix structure, and Daft (1978) and Mintzberg’s (1979) concept of adhocracy
(Sethi and Sethi 1990; Zhang et al. 2002b; De Toni and Tonchia 2005). In certain applications,
decentralization, divisionalization and project management enable firms to increase customer
responsiveness (Child, 1972).
In the context of flexible technologies new organizational forms have been evolving such as the
concept of labor flexibility defined by Attkinson (1985) and product-focused forms among others
defined by Kolodny (1989) which are capable of much faster responses to a changing environment
than traditional hierarchical or functional structures (Sethi and Sethi 1990).
Page 14 of 91
Attkinson (1985) has distinguished three main types of labor flexibilities, namely: numerical
flexibility (the willingness with which the number of people employed can be adjusted to meet
fluctuations in the level of demand), functional flexibility (the extent to which the tasks performed by
workers can by changed in response to varying demand) and financial flexibility (the extent to which
compensation practices stimulate and support numerical and functional flexibility). Product-focused
forms as group technology cells, parallel assembly cells, flexible focused factories, plant-within-
plants and network organizations are inherently more responsive because they are organized around
output forms rather than organized around inputs that are internally focused (Sethi and Sethi 1990).
At the micro-organizational level the organizational approach deals with job enrichment/enlargement
concepts and compensation/incentive practices (De Toni and Tonchia 2005). The importance of the
organizational perspective can be seen in Upton (1995) who argues that plant flexibility depends more
on the people than on the technical equipment and computer integration and Suarez et al. (1995)
describes that flexibility has less to do with technology itself but with non-technology factors (De
Toni and Tonchia 2005).
2.2.2.3 Manufacturing perspective
Finally from the manufacturing (management) perspective or operational perspective, four phases can
be distinguished. The first phase concerns the early conceptualization of flexibility in the context of
manufacturing. According to Diebold (1952) flexibility is essential for the manufacturing of discrete
parts in the medium and short-run. As a break to the traditional literature of machine design, which
had the product rather than the operation in view, Leaver and Brown (1946) and Diebold (1952)
suggest machine design in terms of functions that can be performed and they propose a series of small
functionally oriented machines that can be attached together (Sethi and Sethi 1990; Zhang et al.
2002b). According to Abernathy (1978), Hayes and Wheelwright (1984) flexibility in practice is
viewed as a trade-off against efficiency in production and dependability in the market place (Sethi and
Sethi 1990; Zhang et al. 2002b).
In the early 1970’s with the development of flexible manufacturing systems the enlargement of
flexibility in large scale production without sacrificing efficiency was started (Sethi and Sethi 1990;
Zhang et al. 2002b). In the late 1980’s manufacturing flexibility, which has many types, is one of the
strategic dimensions as a response to environmental uncertainty. It is unclear what the meaning of
manufacturing flexibility is because there is no consensus. Obvious is that a plant may voluntarily
give up one form of flexibility to strengthen another form of flexibility to stay competitive (Avittathur
and Swamidass 2007).
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In the second phase during the 90’s the concept of manufacturing flexibility from the 80’s developed
through a couple of more in-depth flexibility studies. This led to the conclusion that flexibility is a
multidimensional concept. Several dimensions of flexibility are investigated for instance by Upton
(1994; 1995) who distinguishes between potential flexibility and demonstrated flexibility and between
internal and external flexibility and Gerwin (1993) who defined seven dimensions of flexibility.
However through many empirical studies on this subject there is a lack of consensus and cumulative
knowledge building or convergence on manufacturing flexibility (Avittathur and Swamidass 2007).
Unlike the issues of dimensions, there is consensus about the strategic role of manufacturing
flexibility (Avittathur and Swamidass 2007). Another line of research in this period considers the
strategic and marketing view of manufacturing flexibility (Sethi and Sethi 1990; Upton 1994).
In the third phase, as a reaction on the growing supply chain management movement in the late
nineties, supply chain flexibility becomes the focus of investigations. According to Gunasekaran et al.
(2001) supply chain flexibility is the “flexibility to meet particular customer needs in the supply
chain”. Das and Abdel-Malek (2003) define supply chain flexibility from the focus on the existing
supply chain structure and the existing durable relationships as the “elasticity” of the buyer-supplier
relationship under changing supply chain circumstances. According to Pine (1997) and Lummus
(2003) the importance of supply chain flexibility has increased because of the growing importance of
mass customization, which led to a strive for increased supply chain flexibility without increasing
costs (Avittathur and Swamidass 2007). This subject is still in development and is the focus of this
research proposal.
In the fourth and most recent phase the interest has also turned to matching buyer and supplier
flexibilities, for instance Kalwani and Narayandas (1995) describing the importance of flexible
employees in supplier organizations.
2.2.3 Dimensions of flexibility
Flexibility is a complex concept partly because of its multidimensional construct. Many types or
dimensions of flexibility have been identified in the existing literature. It is however important to
know that these definitions are not standardized or widely accepted. There is an overlap between these
flexibility dimensions and different names are used to the same dimensions, which led to some more
confusion. Furthermore the dimensions become more or less important depending on the specific
environment in which they are used. The dimensions are not necessarily correlated. In other words,
having one dimension of flexibility does not mean being flexible in another area or dimension.
When flexibility and in particular manufacturing- and supply chain flexibility are analyzed in
dimensions there can be found a lot of variation in the existing literature.
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There are at least fifty different dimensions of flexibility defined in the manufacturing literature
(Sethi and Sethi 1990). In 1982 Gerwin was the first to mention the various dimensions of flexibility
in a specific way. In the following years (1987; 1993) Gerwin relates these dimensions to different
types of environmental uncertainties which caused them and this led to a framework consisting of
seven dimensions of flexibility (De Toni and Tonchia 1998). Slack (1983) describes, in that same
period, five dimensions of flexibility (De Toni and Tonchia 1998; Slack 2005a).
Another often used classification in the literature is the one by Browne et al. (1984) which considers
eight dimensions of flexibility while considering the Flexible Manufacturing Systems (FMS) (De
Toni and Tonchia 1998). On the basis of the classification of Browne et al. Sethi and Sethi (1990)
distinguishes 11 dimensions of flexibility. Vokurka and O’Leary-Kelly (2000) expanded these
different dimensions developed by Browne et al, and Sethi and Sethi (1990) to fifteen identified
dimensions of flexibility, while Koste and Malhotra (1999) and Narasimhan and Das (2000) identified
ten dimensions in reviewing previous manufacturing literature.
The flexibility dimensions mentioned in the manufacturing literature play an important role in supply
chain flexibility. Beyond the internal flexibilities within the traditional boundaries of the firm, supply
chain flexibility must also have an external perspective. A limited number of authors have started to
discuss flexibility from this supply chain perspective. Vickery et al. (1999) defined five supply chain
flexibilities based on previous literature on manufacturing. However by coupling the responsibilities
for these five dimensions of supply chain flexibility with a specific area of the particular firm (from an
internal perspective) much of the contribution of the supply chain perspective is lost.
It becomes clear that it is very difficult to take into account the cross functional and cross business
nature of the supply chain in defining supply chain flexibility dimensions. In an attempt to develop a
supply chain flexibility model Duclos et al. (2003) identifies six dimensions of flexibility, which are
refined to five by Lummes et al. (2003). Zhang (2002b) defines four parts or dimensions of supply
chain flexibility. Punjawan (2004) based on Swafford et al. (2000) also uses four dimensions of
flexibility. Garvelli (2003) uses two different dimensions of supply flexibility, namely process
flexibility and logistics flexibility in analyzing the supply chain.
In conclusion each author could use different dimensions of supply chain flexibility. However it’s
important that these dimensions should relate to the characteristics and the functions of the supply
chain (Pujawan 2004). Therefore the model of supply chain flexibility studied and tested in this thesis
includes dimensions related to the goods-, information- and money flows and the organizational
structure and strategy behind them (see also figure 1 on page 6).
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In table 1 and 2 the mentioned dimensions of manufacturing flexibility and supply chain flexibility
are summarized. In appendix 1 (overview dimensions of manufacturing flexibility) and appendix 2
(overview dimensions of supply chain flexibility) the whole overview, including the definitions is
given.
Researchers Year Number of
dimensions of
flexibility
Dimensions of flexibility
Gerwin 1987/1993 7 Mix flexibility, Changeover flexibility, Modification
flexibility, Volume flexibility, Rerouting flexibility,
Material flexibility, Sequencing flexibility.
Browne et al. 1984 8 Machine flexibility, Product flexibility, Process flexibility,
Operation flexibility, Routing flexibility, Volume
flexibility, Expansion flexibility, Production flexibility.
Slack 1987 5 Product flexibility, Volume flexibility, Delivery
flexibility, Mix flexibility, Quality flexibility.
Sethi and Sethi 1990 11 Machine flexibility, material handling flexibility,
operation flexibility, process flexibility, production
flexibility, routing flexibility, volume flexibility,
expansion flexibility, program flexibility, production
flexibility, market flexibility.
Vokurka and
O'Leary Kelly
2000 15 Machine flexibility, Material handling flexibility,
Operations flexibility, Automation flexibility, Labor
flexibility, Process flexibility, Routing flexibility, Product
flexibility, New design flexibility, Delivery flexibility,
Volume flexibility, Expansion flexibility, Program
flexibility, Production flexibility, Market flexibility.
Koste and
Malhotra
1999 10 Machine flexibility, Labor flexibility, Material handling
flexibility, Routing flexibility, Operation flexibility,
Expansion flexibility, Volume flexibility, Mix flexibility,
New product flexibility, Modification flexibility.
Narashim and Das 2000 10 Equipment flexibility, Material flexibility, Routing
flexibility, Material handling flexibility, Program
flexibility, Mix flexibility, Volume flexibility,
Modification flexibility, New product flexibility,
Market/delivery flexibility.
Table 1: Dimensions of manufacturing flexibility
Researchers Year Number of
dimensions of
flexibility
Dimensions of flexibility
Vickery et al. 1999 5 Product flexibility, Volume flexibility, Launch flexibility,
Access flexibility, Responsiveness to market(s)
Duclos et al. 2003 6 Operations system flexibility, Market flexibility, Logistics
flexibility, Supply flexibility, Organizational flexibility,
Information systems flexibility.
Lummes et al. 2003 5 Operational systems flexibility, Logistics processes
flexibility, Supply network flexibility, Organizational
design flexibility, Information systems flexibility
Zhang 2002 4 Product development flexibility, Manufacturing
flexibility, Logistics flexibility, Spanning flexibility
Punjawan 2004 4 Sourcing flexibility, New product flexibility,
Manufacturing/production flexibility, Delivering
flexibility
Garavelli 2003 2 Process flexibility, Logistics flexibility
Table 2: Dimensions of supply chain flexibility
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2.2.4 Different aspects of flexibility
Several authors (Gustavsson 1984; Carlsson 1989; Upton 1994; Das and Narasimhan 2000; Jack and
Raturi 2002) have pointed out the differences between operational (or short term), tactical (or medium
term) and strategic (or long term) flexibility. Operational flexibility refers to the short term in which
all hardware capital (plant, equipment) and accompanying software (for instance procedures) is fixed.
Tactical flexibility includes decisions made before the plant is build and the organizational setup is
fixed. In this phase the key strategic decisions, for instance which products should be made, have been
taken. Strategic flexibility on the long term is about positioning the firm in the future, such as the
product types the firm wants to make, the position on the market it wants to have, adjusting its
strategies, etc. (Carlsson 1989; Upton 1994).
Other studies have pointed out the relationship between the different flexibility dimensions and come
to a hierarchy of flexibility dimensions, also called a vertical classification. This hierarchy consists of
different tiers in which the lower tiers, which are more tactical, contain the flexibility dimensions that
serve as building blocks for the flexibility dimensions in the upper tears, which are more strategic.
Swamidass (1987) distinguishes a
machine level flexibility and plant level
flexibility. Gerwin (1987) describes
four levels of flexibility, the machine-,
production function-, product (line)-
and global firm level.
Slack (2005a) first introduces the
concept of flexibility hierarchy and
describes four categories;
manufacturing resources, the aim of
production, the production function and
the whole company (De Toni and
Tonchia 2005; Slack 2005a). Huhn and
Ahn (1992) came to three tiers of
flexibility and Koste and Malhotra
(1999) developed a five tier hierarchy,
see figure 2.
Figure 2: Hierarchy of flexibility dimensions (Koste and Malhotra, 1999)
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Flexibility can also been classified to internal flexibility and external flexibility (Wheelwright, 1984)
(Upton 1994; Vickery et al. 1999; D'Souza and Williams 2000; Chang et al. 2003; Chang et al. 2005).
External flexibility is related to the market, the needs of the customer and thus to a firm’s competitive
advantage. Suarez et al. (1996) refers to it as “first order” flexibility (Chang et al. 2006) and
Stevenson and Spring (2007) as flexibility at the interfirm level. Internal flexibility is not direct
related to the market demand and environmental uncertainties. It is related to meet the customer the
customer requirement and thus the external flexibility in an efficient way. Stevenson and Spring
(2007) refers to it as flexibility on a intrafirm level. Supply chain flexibility includes both components
of internal and external flexibility and combines flexibility inherent at the interfirm level together with
those of the intrafirm level.
2.2.5 Flexibility versus agility
According to Sharifi and Zhang (2001) agility is a new paradigm being promoted as the solution to
maintain competitive advantage. They define agility as a “the ability to cope with unexpected
challenges, to survive unprecedented threats of business environment, and to take advantage of
changes as opportunities (1999). Flexibility and mainly external flexibility are as well as agility
related to the market and the customers’ needs and thus to a firm’s competitive advantage. According
to the definition of flexibility from Mandelbaum and Buzacott (1990), flexibility can also been seen as
the ability to respond effectively to changing circumstances.
Looking to these definitions flexibility and agility look the same, but a distinction between flexibility
and agility does exist (Narasimhan et al. 2006; Swafford et al. 2008). One distinction comes from the
resource-based view where agility is defined as core competence that relies on various capabilities,
especially various forms of flexibility (Vokurka and Fliedner 1998b; Agarwal et al. 2006). In other
words agility in the resource based view can be achieved by combining the synergies among different
forms of flexibility within firms. Secondly flexibility relates to adaptability and versatility (Kidd
2000; Prater et al. 2001) and agility is more focused on speed or the time required to adapt (Swafford
and Murthy 2000; Prater et al. 2001; Swafford et al. 2008). Agility therefore is a measure of reaction
time where flexibility is a measure of reaction capabilities.
Given these differences, agility and flexibility are distinct concepts by which flexibility is an
antecedent of agility taking into account that flexibility can exist without agility but agility cannot
exist without flexibility (Swafford and Murthy 2000). The different forms of flexibility in the supply
chain are studied in this thesis.
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2.3 Supply Chain flexibility
In this paragraph the concept of flexibility and especially the concept of supply chain flexibility used
in this thesis will be described based on the literature on flexibility in paragraph 2.2.
The definition of flexibility in this thesis is composed using the definitions of Upton (1994; 1995)
who described flexibility as “the ability to change or react to environmental uncertainty with little
penalty in time, effort, cost or performance” and Zhang et al. (2002b; 2002a; 2006) who defined
flexibility as “the organizations ability to meet an increasing variety of customer expectations without
excessive costs, time and organizational disruptions or performance losses”. The composition of both
definitions led to a more comprehensive definition of flexibility that will be used in this thesis:
“Flexibility is the organizations ability to change or react to environmental uncertainty and to meet
the increasing variety of customer expectations without excessive costs, time and organizational
disruptions or performance losses”.
The concept of flexibility can further be clarified as having three distinctive elements, namely
range/variety, mobility/responsiveness and uniformity/procedures as described in paragraph 2.2.1.
Another distinction can be made between internal or potential flexibility (what a firm can do) and
external or manifested flexibility (what the customer sees). These elements and aspects underlie each
dimension and accompanying components of value chain flexibility or supply chain flexibility in the
described concept of supply chain flexibility.
In this thesis supply chain flexibility is looked at from the customer perspective, the type of flexibility
the customers want to fill their orders. From this perspective the efforts between organizations and
functions in the supply chain are needed to increase responsiveness and to make it possible for a firm
to build up competitive advantage (Zhang et al. 2002b; Zhang et al. 2002a). To be effective and fulfill
this goal the definition of supply chain flexibility should be broad and should include the following
dimensions (Day 1994; Zhang et al. 2002b; 2002a; 2003; 2005; 2006):
• Product development flexibility;
• Manufacturing flexibility;
• Logistics flexibility;
• Spanning flexibility.
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Each dimension of supply chain flexibility can be divided into competences and capabilities, which
are linked with the internal and external aspects of flexibility and correspond to primary and
secondary flexibility from Watts et al. (1993) (Zhang et al. 2002b). A firm can achieve customer
satisfaction and build competitive advantage by developing these competences that lead to the needed
capabilities (Day 1994; Teece et al. 1997). A capability in this perspective is the linkage between
corporate, marketing and manufacturing strategy. And a competence provides the processes and
infrastructure to enable the firm to achieve these desired (levels of) capabilities (Zhang et al. 2002b).
In the following paragraph the comprehensive conceptual model including the dimensions of supply
chain flexibility and the components which are based on the competence and capability approach, will
be described.
2.4 Conceptual model
Figure 3 on the following page represents a schematic diagram of the comprehensive conceptual
model based on previous research of Zhang et al. (2002b; 2002a; 2003; 2005; 2006) (see also
appendix 3). The major parts of this conceptual model are the four dimensions of supply chain
flexibility according to Day (1994) and Zhang et al. (2002b; 2002a; 2003; 2005; 2006) in relation to
customer satisfaction.
In this thesis only a part of this conceptual model, the capabilities in relation with customer
satisfaction, will be studied and tested (see figure 3, inside the red lines). From a customer oriented
perspective all activities within a supply chain should be focused on satisfying customers’ needs
(Kumar et al. 2006), because a happy and satisfied customer is one of the most important things for a
worldwide company (Gunasekaran et al. 2004). Therefore it is important that the supply chain
flexibility research model encompass those flexibilities that directly impact a firm’s customers
(Kumar et al. 2006). These flexibilities are called the external or manifested flexibilities and in the
conceptual model they are according to Day (1994) and Zhang et al. (2002b; 2002a; 2003; 2005;
2006) presented as external or customer pleasing capabilities.
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Figure 3: Conceptual model Supply Chain Flexibility
To give an overview all the dimensions of the conceptual model including the accompanying
components or sub-dimensions will be described in paragraph 2.4.1. A definition of customer service
and its measures will be presented in paragraph 2.4.2. In the hypotheses in chapter 3 the linkages
between the capabilities as customer oriented components of supply chain flexibility and customer
satisfaction will be described and the definitive research model of this thesis will be presented.
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2.4.1 Dimensions of supply chain flexibility
The different dimensions of supply chain flexibility are, as described in paragraph 2.3, product
development flexibility, manufacturing flexibility, logistics flexibility and spanning flexibility and
enables firms a quick introduction of new products, support fast product customization, make
manufacturing lead times shorter, lower the costs for customized products, improve supplier
performance, gain inventory reduction and the delivery of products in an efficient, timely and
effective way (Day 1994; Zhang et al. 2002b).
2.4.1.1 Product development flexibility
Product development flexibility is the ability to rapidly and effectively introduce and launch new
(innovative) products and modify existing products in response to customer needs for design changes
(Sethi and Sethi 1990; Suarez et al. 1992; Zhang et al. 2002b; Zhang et al. 2002a; Slack 2005a).
Modifying existing products asks for different skills and abilities than introducing a new product
(Olson et al. 1995; Zhang et al. 2002b; Zhang et al. 2002a) as the market expectations for these new
products and the specifications needed are not clear because customers cannot compare these products
with existing ones and the development can take years. Product development flexibility has four
components: prototype flexibility and product concept flexibility, which are capabilities, and new
product flexibility and modification flexibility, which are the competences that are visible for the
customer.
Product concept flexibility is the ability to rapidly develop ideas and keep set-based product concepts
and definitions (Griffin and Hauser 1996; Zhang et al. 2002b). The definition of prototype flexibility
is the ability to rapidly and cost-efficiently build and modify product samples (Zhang et al. 2002b;
Zhang et al. 2002a). The range of these flexibilities could be established by the number of
concepts/options and prototypes that can be made. Mobility and performance uniformity could be
established by the development time and/or the costs that are made for the quick development of
multiple concepts and prototypes. Developing these competences leads to the capabilities new product
flexibility and modification flexibility.
New product flexibility is the ability to rapidly and effectively introduce and launch new products
(Sethi and Sethi 1990; Zhang et al. 2002b; Zhang et al. 2002a). The definition of modification
flexibility is the ability to rapidly and effectively modify existing products in response to customer
needs for design changes (Sethi and Sethi 1990; Zhang et al. 2002a; Zhang et al. 2002b). The range of
these flexibilities could be established by the number and variety of the products introduced and
launched or modified. Mobility and performance uniformity could be established by the development
time and/or the costs that are made (Zhang et al. 2002b; Zhang et al. 2002a).
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2.4.1.2 Manufacturing flexibility
Manufacturing flexibility is the ability of the firm to manage manufacturing resources and uncertainty
to meet the different customer demands (Chen et al. 1992; Zhang et al. 2002b; Zhang et al. 2003).
Manufacturing flexibility has six components; volume flexibility and mix flexibility, which are
capabilities, and machine-, labor-, material handling- and routing flexibility, which are the
competences.
Machine flexibility is the ability of a piece of equipment to efficiently and effectively perform
different operations (Sethi and Sethi 1990; Chen et al. 1992; Zhang et al. 2002b; Zhang et al. 2003).
The definition of labor flexibility is the ability of the workers to efficiently and effectively perform a
big range of manufacturing tasks (Ramasesh and Jayakumar 1991; Upton 1994). Material handling
flexibility can be defined as the ability to efficiently and effectively transport different parts between
various work centers in multiple ways (Sethi and Sethi 1990; Coyle et al. 1992). Routing flexibility is
the ability to efficiently and effectively process a given set of part types using multiple ways (Sethi
and Sethi 1990; Gerwin 1993; Upton 1995). The range of these flexibilities could be established by
successively the number of operations a machine can perform, the number of tasks a worker can do,
the number of ways the parts can be transported and the number and variety of alternative ways a part
(type) can be processed. Mobility and uniformity could be established by successively the time and/or
the costs for the switching and the setups of the machines and the quality and efficiency of producing
the different products, the effectiveness of the work that is done by the workers, the time and/or the
cost for changing a way and for using this different way and time and/or cost for adding a alternative
and by differences in processing time and quality by using this alternative ways. The development of
machine-, labor-, material handling- and routing flexibilities have a direct positive impact on the
capabilities volume flexibility and mix flexibility as external elements of competition (Zhang et al.
2002b; Zhang et al. 2003).
Volume flexibility is the ability of the firm to efficiently and effectively operate in different batches
and/or different overall output levels (Carlsson 1989; Sethi and Sethi 1990; Gerwin 1993). The
definition of mix flexibility is the ability of the firm to efficiently and effectively produce various
combinations of products (Sethi and Sethi 1990; Gupta and Somers 1992). The range of these
flexibilities could be established by the level of profitable output (under normal circumstances) and
the number of different products produced and the degree of differentiation between those products.
Mobility could be established by the time needed to change the output level and the time and/or cost
to change the product mix. The cost and quality levels of production and the firm’s ability to maintain
product quality and productivity producing various combinations of products measures uniformity
(Zhang et al. 2002b; Zhang et al. 2003).
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2.4.1.3 Logistics flexibility
Logistics flexibility is the ability of the firm to effectively and rapidly respond to customer
requirements for delivery, support and service (Perry 1991; Davis 1993; Day 1994; Bowersox and
Closs 1996; Zhang et al. 2002b; Zhang et al. 2005). So logistics flexibility takes care of a fluent
material flow through manufacturing and a rapidly delivery to the customers. Logistics flexibility has
four components: physical supply flexibility and purchasing flexibility, which are the competences,
and physical distribution flexibility and demand management flexibility, which are the customer
facing capabilities.
Physical supply flexibility is the ability of the firm to rapidly and exactly provide a variety of inbound
and transportation of materials and supplies, warehousing and inventory for production (Langley and
Holcomb 1992; Day 1994; Bowersox and Closs 1996; Narasimhan and Carter 1998; Zhang et al.
2002b; Zhang et al. 2005). The definition of purchasing flexibility is the ability of a firm to rapidly
and effectively make agreements to buy a variety of materials and supplies (Narasimhan and Carter
1998; Porter 1998; Van Hoek 2001; Zhang et al. 2002b; Zhang et al. 2005). The range of these
flexibilities could be established by the number of inbound transportation modes and the variety of
materials supplied, packed and purchased. Mobility is measured by the development time and
efficiency of the different transportation modes and packages and the difference in time and/or the
costs to fulfill the requested variety of materials. Uniformity is assessed by the quality and reliability
of the different incoming goods and the quality of the purchasing process and the materials purchased.
The competences physical supply- and purchasing flexibility has an impact on the customer indirectly
by the quality, speed and cost of the materials that are purchased and the effectively and efficiently
way the materials are supplied (Zhang et al. 2002b; Zhang et al. 2005).
Physical distribution flexibility is the ability of the firm to rapidly and effectively adjust inventory,
packaging, warehousing and transportation of physical products in respond to customer requirements
(Day 1994; Lambert et al. 1998; Van Hoek et al. 1998). The definition of modification demand
management flexibility is the ability of the firm to rapidly and effectively respond to the customer
requirements for delivery time, price and service (Langley and Holcomb 1992; Day 1994; Lee 2001).
The range of these flexibilities could be established by the packaging types and the number of
transportation modes and the customer requirements that can be fulfilled. Mobility is measured by the
time and/or the costs to use various transportation ways and different packages and the differences in
time and/or the costs of demand management. Uniformity is assessed by the quality and delivery
reliability and the quality of the services. The capabilities, physical distribution flexibility and
demand management flexibility enable firms to meet the demand of the customers and is therefore of
strategic importance (Zhang et al. 2002b; Zhang et al. 2005).
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2.4.1.4 Spanning flexibility
Spanning flexibility is the ability of the firm to provide for horizontal information connections within
the supply chain (Day 1994). Spanning flexibility has two components: information dissemination
flexibility as competence and strategy development flexibility as capability. So the focus of spanning
flexibility is the development of supply chain information dissemination flexibility because this is
crucial for the collection and transportation of the different data in the supply chain and the
development of strategy development flexibility (Zhang et al. 2006).
(Supply chain) Information dissemination flexibility is the ability of the firm to rapidly collect and
spread the different data needed along the supply chain to meet customer requirements (Cooper and
Zmud 1990; Bowersox et al. 1999). The range of these flexibilities is assessed by the diversity of data.
The mobility and uniformity is established by the quality of the obtained information and the time to
capture this usable data.
Strategy development flexibility is the ability of a firm to rapidly and effectively develop and recreate
the strategy, consisting of objective and plans, based on the internal competence and the changing
external customer requirements (Wheelwright and Hayes 1985; Hayes and Pisano 1994). The range of
these flexibilities could be established by the diversity of the adjusting and combining skills. The
mobility and uniformity is established by the strategic response time and the performance.
2.4.2 Customer satisfaction
Customer satisfaction has developed around two different perspectives: transaction specific
perspective and the cumulative perspective (Rust and Zaborik 1993; Anderson et al. 1994; Daugherty
et al. 1998; Jones and Suh 2000; Johnson et al. 2002; Zhang et al. 2002a, 2003; Yang and Peterson
2004; Zhang et al. 2005). Shankar et al. (2003) describes them as service encounter satisfaction
(which is transaction specific) and overall customer satisfaction (which is relationship-specific).
Jaiswal and Niraj (2007) defines them according to previous literature as overall satisfaction and
attribute satisfaction.
Transaction specific satisfaction is a customer’s evaluation after offering the product or service that is
purchased. Cumulative or overall satisfaction involves the overall experience of the product or service
concerning the purchase and the use and consumption over a period of time (Anderson et al. 1994;
Daugherty et al. 1998; Johnson et al. 2002; Zhang et al. 2002a; Shankar et al. 2003; Zhang et al. 2003;
Yang and Peterson 2004; Zhang et al. 2005).
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Transaction specific satisfaction and cumulative satisfaction are not competing concepts, they are
complementary. Cumulative or overall satisfaction is an essential indicator of the performance of the
firm in the past, nowadays and in the future (Anderson et al. 1994; Daugherty et al. 1998). Because of
this customer satisfaction in this thesis will be defined from the overall or cumulative perspective.
Overall customer satisfaction can be described as having three elements (Fornell et al. 1996;
Anderson and Fornell 2000; Grigoroudis and Siskos 2004):
• The perceived quality or performance, which is the evaluation of the experienced product or
service concerning customization and reliability;
• The perceived value or the perceived value of product quality in relation to the paid price;
• The customer expectations, based on the information the firm offered to the market and an
estimation of the firm’s ability to deliver quality in the future, so it concerns the expectations
looking after consumption and looking forward for consumption has taken place. The
expectations role is important because the continuation of the relationship between the customer
and the firm is built on this. According to Ittner and Larcker (1998), Mittal and Kamakura (2001),
Auh and Johnsson (2005), Anderson, Paero and Widener (2008), satisfaction affects the
repurchase likelihood and this actual customer retention behavior in positive way.
In several studies the variables that influence customer satisfaction have been described. White (1996)
describes six variables that influence customer satisfaction including quality, delivery speed, delivery
dependability, cost, flexibility and innovation. Koufteros et al. (2002) identified seven variables
(or measures of competitive capabilities) which are cost, competitive- and premium pricing, value to
customer quality, product mix flexibility, product innovation and customer service. Similar variables
are used by Tracey et al. (1999) and Tracey and Tan (2001): price offered quality of products or
competitive pricing and product quality, product line breadth or product variety, order fill rate and
frequency of delivery or delivery service. Zhang et al. (2002a; 2003; 2005; 2006) describes five
measures for customer satisfaction which include retention, ratio of price to value, quality, product
reputation and loyalty.
The four variables that will be used in this thesis based on the three distinctive elements of customer
satisfaction are quality, price-value ratio, product reputation and retention or relationship
continuation. Customer loyalty is not part of this because customer loyalty and satisfaction are not
replaceable. A customer can be highly satisfied but not loyal because there are many alternatives
available (Shankar et al. 2003; Auh and Johnson 2005). In reverse a loyal customer would not have to
be highly satisfied, for instance through high switching barriers (Andreassen and Lindestad 1998;
Shankar et al. 2003).
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3. Hypotheses and research model
The common theoretical background is described in paragraph 2.4 on the base of the conceptual
model. This theory will be used in this paragraph to formulate some hypotheses that relate flexible
capabilities as customer oriented components of supply chain flexibility to customer satisfaction.
After describing these hypotheses the final research model will be presented.
3.1 Hypotheses
Supply chain flexibility can provide a variety of innovative, low cost, high quality products in the
appropriate amount on the right moment at the right place to foresee in the customers demand.
This flexibility comes not only from a function as logistics flexibility but from the integration of
flexibility along the supply chain, which consists of product development-, manufacturing-, logistics-
and spanning flexibility (Zhang et al. 2002b).
With flexible product development, firms can quickly respond to a rapidly changing environment with
product modification and new product commercialization. Such flexible design and modification
capabilities can increase the manufacturing ability by simplifying product structure, reducing the
number of parts and standardizing parts (Clark and Fuijmoto 1991). This results in an easier and faster
manufacturing process in which product quality can be easier controlled. In this flexible system it will
also be easier to bring a production back to tolerances when a new production run begin, because the
changeovers can be made easier and faster (Leong et al. 1990). This including with flexible logistics
capabilities facilitates the manufacturing process by delivering high quality materials on time and
afterwards delivering the right product at the right moment to the customer. With spanning flexibility
this whole process can be coordinated by different groups to assure the delivery of good quality
products in the right amount, at the right place and at the right moment to satisfy the customer.
Being flexible in the supply chain by having these capabilities to provide products and/or services that
meet the individual demands of customers lead to increased customer satisfaction (Beamon 1999;
Gunasekaran et al. 2004). So the preposition of this thesis is that product development flexibility,
manufacturing flexibility, logistics flexibility and spanning flexibility have a positive impact on
customer satisfaction.
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3.1.1 Product development flexibility capabilities and customer satisfaction
Flexible product development is demanded, because when a product is under development the
required technologies as well as the customers’ need the product is supposed to fulfill can change
dramatically. Through flexible product development it must be possible to develop products quickly
to the market and customer needs, or in other words the firm can quickly anticipate to the customers’
needs or requirements (Matzler and Hinterhuber 1998). It should even be possible to define and
(re)shape the products to customers’ wishes after implementation has begun. So flexible product
development must enable firms to adapt rapidly to evolving customer requirements and changing
technologies by modifying or developing the designs until the last possible moment before a product
is introduced to the market (Thomke 1997; MacCormack et al. 2001; Zhang et al. 2002a).
Flexibility in modifying existing products and/or in commercializing new products allows firms to
better meet the customers’ needs by improving current products and maintaining the depth and
breadth of a firm’s product portfolio. Customers’ satisfaction will increase when the products are
fulfilling the needs of the customer and are of good quality, obtainable in a short time frame and at
reasonable costs:
H1. Product modification flexibility has a positive impact on customer satisfaction.
H2. New product flexibility has a positive impact on customer satisfaction.
3.1.2 Manufacturing flexibility capabilities and customer satisfaction
Volume- and mix flexibility are important customer oriented and organizational capabilities that must
be planned and managed effectively to achieve customer satisfaction. Firms can achieve high levels of
customer satisfaction by delivering high value, which results in customers who are likely to
repurchase and, thus, long-term continuating relationships which foresees in a base of steady clients
(Innis and La Londe 1994; Slater and Narver 1995; Narver et al. 1998; Zhang et al. 2003; Sánchez
and Pérez Pérez 2005; Hallgren and Olhager 2009).
Volume flexibility enhances customer satisfaction by producing the exact amount of product required
and ordered. Through volume flexibility the firm can increase product volume quickly in response to
unanticipated needs and reduce volume quickly to avoid building inventory (Jack and Raturi 2002;
Oke 2003). In that way volume flexibility results in reduced or eliminated waiting times for
customers when the demand level fluctuate and it reduces the costs/prices by lowering inventory in
the supply chain (Zhang et al. 2003).
H3. Volume flexibility has a positive impact on customer satisfaction.
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Mix flexibility enables firms to satisfy their customers by producing the product with the features and
performance the customer want. Through mix flexibility a wide variety of products can been produced
without excessive time delays, premium prices or declines in quality. The waiting time for special
products that are of high value for customers are reduced by mix flexibility (White 1996; Kathuria
2000; Bengtsson and Olhager 2002; Zhang et al. 2003; Wahab 2005).
H4. Mix flexibility has a positive impact on customer satisfaction.
3.1.3 Logistics flexibility capabilities and customer satisfaction
Flexible and fast responses play an important role in enhancing customer service, which combines the
benefits of customer satisfaction, loyalty and increased sales (Emerson and Grimm 1998). To foresee
in this flexible and fast responses to fully serve the customer the processes of information processing
and efficient material handling becomes very important (Damen 2001).
Physical distribution flexibility enhances customer satisfaction by offering customized services at a
competitive cost to the final customer. These services could include activities as packaging, final
assembly, product configuration, inventory management and transportation (Van Hoek 2001).
Through physical distribution as the final link to the customer the supply chain can adapt to the
changing market and customer requirements to satisfy the customers (Zhang et al. 2005).
H5. Physical distribution flexibility has a positive impact on customer satisfaction.
Demand management flexibility enables firms to satisfy their customer by responding quickly and
effectively to the customers’ needs for service, delivery time and price. It is a market sensing and
information sensitive capability that must meet demands quickly by creating and managing close
customer relationships (Day 1994; Lee 2001).To gather these customer requirements firms must
maintain direct customer contact, collect information about customer needs and use customer supplied
information to design and deliver products and services the customer needs (Schneider and Bowen
1999; Zhang et al. 2005).
H6. Demand Management flexibility has a positive impact on customer satisfaction.
3.1.4 Spanning flexibility capability and customer satisfaction
Strategy development flexibility is a market sensing and customer linking capability that develops and
manages customer relationships in which firms and customers can share interdependences, values and
strategies (Day 1994).
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On the base of this accumulated information the firm can adapt and integrate its organizational skills,
functional competences and resources to match the requirements of the changing environment (Teece
et al. 1997; Zhang et al. 2006).
So with strategy development flexibility a firm can (by acting on timely information and using
advanced information technologies) quickly coordinate source, make and deliver operations that
enable quick reaction to satisfy changing customer needs (Feitzinger and Lee 1997; Cooper and Pagh
1998; Zhang et al. 2006).
H7. Strategy development flexibility has a positive impact on customer satisfaction.
3.2 Research model
The framework for this thesis is presented in figure 4 (and appendix 4).
Figure 4: Research model
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The arrows point out the relationships between the capabilities of the mentioned dimensions of supply
chain flexibility and customer satisfaction and match the described hypotheses H1 till H7:
H1. Product modification flexibility has a positive impact on customer satisfaction.
H2. New product flexibility has a positive impact on customer satisfaction.
H3. Volume flexibility has a positive impact on customer satisfaction.
H4. Mix flexibility has a positive impact on customer satisfaction.
H5. Physical distribution flexibility has a positive impact on customer satisfaction.
H6. Demand Management flexibility has a positive impact on customer satisfaction.
H7. Strategy development flexibility has a positive impact on customer satisfaction.
These hypotheses will be statistically tested in the following chapter.
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4. Methodology
In the previous chapter the hypothesis are described and the final research model is presented.
In this chapter the research methodology to collect the empirical data that is needed to make it
possible to test these hypotheses will be described. The research methodology consists of determining
the purpose of the research, select a suitable research design and choose the appropriate data
collection method (Flynn et al. 1990).
The theoretical foundation of the research methods will be presented in paragraph 4.1. In paragraph
4.2 the selected research design is described and in paragraph 4.3, the data collection method will be
explained. A description of the implementation of the research methodology is given in paragraph
4.4, describing the chosen sample, the used instruments and the response rates.
4.1 Theoretical Foundation
In the literature there is made a distinction between three purposes of research: exploratory studies,
descriptive studies and explanatory studies (Saunders et al. 2007), also called exploratory, descriptive
and causal research by Ghauri and Gronhaug (2005) and Cooper and Schindler (2003). Baarda et al
(2001) describe these elements as exploratory, descriptive and testing research.
• Exploratory research or studies are used to find out what is happening, to find new insights and to
ask questions and determine phenomena in a new light (Saunders et al. 2007). The research
problem is not clear and the research is unstructured (Ghauri and Gronhaug 2005). It inclines
loose structures with the goal of discovering future research tasks by developing hypotheses or
questions (Baarda and De Goede 2001; Cooper and Schindler 2003). During the research when
new information is released the problem becomes clearer and clearer and at the end the insights
let to a obvious understanding of the problem and often to certain hypotheses or questions which
is the primary goal of this research or studies (Ghauri and Gronhaug 2005).
• The object of descriptive research or studies are to produce an accurate summary of persons,
events or situations without searching for any further relations or explanations (Baarda and De
Goede 2001; Saunders et al. 2007). The research problem is structured and well understood
(Ghauri and Gronhaug 2005). Without the aim to develop a theory or formulate hypotheses this
research include registration and systematic ordening of all what occurs on a specific area,
following a predetermined procedure (Baarda and De Goede 2001). Key characteristics therefore
are exact rules, structure and procedures (Ghauri and Gronhaug 2005).
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• Explanatory, causal or testing research or studies focus on studying a situation or problem in order
to explain the relationship(s) between variables, which are described in hypotheses or frameworks
(Baarda and De Goede 2001; Saunders et al. 2007). The research problem is structured and well
understood (Ghauri and Gronhaug 2005). The difference with descriptive research is the
objective. Descriptive research concerns questions as what, where, when and how much while
explanatory, causal or testing research are trying to find relationships and explanations in asking
the question why-that is (Cooper and Schindler 2003).
The goal of this thesis is to determine the relationship between supply chain flexibility capabilities
and customer satisfaction as described in the hypotheses in the previous chapter. By testing these
hypotheses this study can be seen as an example of exploratory or testing research. So this research
will be used to verify the theory by testing the hypotheses using the data collected (Flynn et al. 1990).
4.2 Research Design
After determining the purpose of the research, the research design sometimes called the research
strategy can be selected. A number of designs are identified in the literature. Without being complete
the following designs can be defined (Flynn et al. 1990; Baarda and De Goede 2001; Saunders et al.
2007):
• Case study (single case study and multiple case studies)
• Experiment
• Panel Study
• Focus Groups
• Survey
To (statistically) test the described hypotheses a sufficient number of observations of data should be
available (Mentzer and Kahn 1995; Mentzer and Flint 1997). A survey is the most commonly used
research design in this studies, because it allows collection of a large amount of standardized data
from a sizable population in an economical way and the collected data can be easily compared
(Saunders et al. 2007). A survey relies on self-reports, facts as well as opinion (Flynn et al. 1990).
To measure the constructs of supply chain flexibility capabilities and customer satisfaction, 5 or 6
items pro construct should be answered from a person’s individual experience and knowledge.
According to the Meredith model (Dunn et al. 1994) survey research can be used in this situation.
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4.3 Data Collection Method
There are different data collection methods that can be used by using a survey strategy, such as
structured observation, interviews and questionnaires (Saunders et al. 2007). Because of the large
amount of structured data needed to statistically testing, the large number of respondents that
therefore should be approached and the little information available from respondents, the
questionnaire is selected to collect the data needed to test the above hypotheses (Cooper and Schindler
2003; Saunders et al. 2007). Besides this (Cooper and Schindler 2003):
• A questionnaire allows contact with otherwise inaccessible contacts;
• It is less expensive (it costs less time and money to reach a large sample);
• A questionnaire is perceived as more anonymous;
• It allows respondents time to think about the questions;
• The data can easy be worked out used for analyzing and testing.
Although a questionnaire is the most common choice to collect the data needed in this thesis, it has
also some disadvantages:
• No interviewer intervention available for explanation;
• The questionnaire may not be too long, so the measurement items are limited;
• Accurate mailing lists are needed;
• Preparation time is higher.
4.4 Implementation
This paragraph includes a description of the implementation of the chosen research method in
practice. First the sample that is chosen in this thesis will be described. In paragraph 4.4.2 the
instruments which will be used to measure the described capabilities of flexibility and customer
satisfaction for testing the hypotheses will be presented. At last the response rates of this survey are
given in paragraph 4.4.3.
4.4.1 Sample
In order to test the mentioned hypotheses the sample that is chosen consists of a number of “big”
manufacturing companies in the Netherlands with one hundred or more employees. This demarcation
is made to increase the chances a firm has separate functions for product development, manufacturing,
logistics and spanning as required in this study. According to Centraal Bureau for the Statistiek there
are 6945 companies with one hundred or more employees in the Netherlands (on date 18-1-2009). Not
all of these companies have manufacturing activities.
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To get a sample which consists only of “big” manufacturing companies in the Netherlands a selection
is made using the database Amadeus. The selection is made using the following three criteria:
� Geographic Location: The Netherlands,
� Employees: 100 employees or more
� Industry: Nace Rev. 2 Sections => C. Manufacturing
At the end this led to a sample of 1000 companies active in different sectors with the SIC codes
varying from 20 till 39 (see appendix 5 for an overview of these SIC codes). All these 1000
manufacturing companies obtained by limited tracking in Amadeus are part of the study, because of
the differences between the companies and the little information available from the respondents the
sample should be as large as possible to be representative (Mentzer and Kahn 1995; Cooper and
Schindler 2003).
4.4.2 Instruments
To set up the questionnaire that will be sent to the sample of 1000 companies, Dillman’s tailored
design method (Dillman 2007; Dillman et al. 2009) is used including the use of an incentive. The
questions used to measure the different components of flexibility and customer satisfaction are based
on Zhang et al. (2002b; 2002a; 2003; 2005; 2006) (see appendix 6). Each dimension of flexibility and
also customer satisfaction is measured by five or six questions using a five point Likert scale with 1=
strongly disagree, 2= disagree, 3= neutral, 4= agree and 5= strongly agree.
Before sending the survey to these companies the questionnaire was pilot tested by examination
through ten colleagues with a university or management background (Flynn et al. 1990; Dillman
2007; Dillman et al. 2009).
On the base of Dillman’s tailerod design method (Dillman 2007; Dillman et al. 2009) a system of
multiple compatible contacts was used to approach the companies in the sample, including:
• A prenotice informing mail sent to the respondents a few days before the questionnaire.
• A hardcopy questionnaire including an invitation letter and return envelope with response number
sent by post.
• A mail with the URL-link to the questionnaire on the internet.
• Thank you/reminder mail sent after one week.
• A second mailing which consists of a reminder mail with the URL-link to the questionnaire on the
internet sent after two weeks after the first mailing.
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4.4.3 Response rates
Out of 100 responses received (3 undeliverable,
8 blank returns and 6 incomplete), 83 were usable
resulting in a response rate of 8,3 %. This response
rate is comparable with the response rates in some
previous studies of supply chain flexibility such as
Zhang et al. (2002a; 2003; 2005; 2006) and less high
than previous studies on different flexibilities such as
manufacturing flexibility with response rates
between 10 % till 35% such as Koste, Malhotra and
Sharma (2004).
Respondents were managing representatives
including president/CEO, vice president, (general)
manager, director, production manager, logistics
manager and others, namely purchasing manager,
marketing manager, supply chain manager or
-specialist (see also appendix 7).
The number of responses across the SIC codes
20 till 39, the firm sizes (measured by the number of
employees) and the job titles are shown in table 3.
SIC code 34, 20, 35 and 36 which are the
manufacturing of fabricated metal products, food
products, industrial and commercial machinery
and computer equipment and electrical equipment
have the highest percentages in responses. 40% of the responses come from firms with 100-249
employees, and 68% of the firms have less than 1000 employees. Most respondents are logistics
managers and directors. An overview of the other jobs that respondents of the survey fulfill (almost
44% of all responses) is presented in appendix 7.
Table 3: Overviews responses
SIC: Respondents Percent
20 11 13%
22 1 1%
23 4 5%
24 2 2%
25 1 1%
26 3 4%
27 1 1%
28 8 10%
29 1 1%
30 3 4%
32 4 5%
33 2 2%
34 15 18%
35 9 11%
36 8 10%
37 8 10%
38 2 2%
Firm size (number of employees): Respondents Percent
100-249 32 39%
250-499 21 25%
500-999 10 12%
1000-2499 7 8%
2500-4999 3 4%
5000-7499 3 4%
7500-9999 3 4%
10000+ 4 5%
Position Respondent: Respondents Percent
CEO/President 5 6%
Vice President 3 4%
General Manager 8 10%
Director 10 12%
Production Manager 1 1%
Logistics Manager 21 25%
Other 35 42%
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5. Results
This research develops a set of valid and reliable instruments to measure the six external or customer
facing capabilities of supply chain flexibility and its impact on customer satisfaction from the view of
the manager.
The cross sectional data is analyzed using SPSS 17.0 for windows and SmartPLS. The reliability
analyses result for each construct are reported first. Then the results of the hypothesis will be
described using descriptive statistics, correlation analyses, multiple regression analyses and structural
equations modeling (SEM).
5.1 Reliability analyses
Reliability analyses are used to determine if the internal coherence between the individual items is
high enough to measure the different constructs of flexibility and customer satisfaction. An overview
of the reliability analyses of the six customer facing capabilities and customer satisfaction can be
found in table 4. In appendix 8 the complete reliability analysis of each construct of flexibility and
customer satisfaction is given.
Construct Items Crombach's alpha
Product Modification Flexibility 5 0,820
New Product Flexibility1 4 0,826
Volume Flexibility 6 0,772
Mix Flexibility 6 0,882
Physical Distribution Flexibility 6 0,813
Demand Management Flexibility 5 0,690
Strategy Development Flexibility 5 0,806
Table 4: Overview reliability analysis of each construct of flexibility and customer satisfaction
With a crombach’s alpha around 0,7 for demand management flexibility and around 0,8 for all other
constructs of flexibility and customer satisfaction the items of the flexibility constructs and the
customer satisfaction construct can be considered as internally consistent and sufficient for basic
research (Nunally and Bernstein 1994; Baarda et al. 2007).
In table 5 on the next page the descriptive statistics and measurement items are presented.
1 Item NP 2 is dropped because of the negative and low correlation loadings.
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Table 5: Descriptive statistics on item level2
2 Absolute Z-values are presented. Significant skewness and kurtosis if -1,99 < absolute Z-value < 1,99
Construct Items Mean St. Dev. Loading t-value Z-value
skewness
Z-value
kurtosis
We can quickly modify product design in response to customer requests 3,35 1,02 0,843 4,044 -,326 -,785
We can easily modify products to a specific customer need 3,47 1,04 0,826 4,471 -,318 -,922
We can better meet customer needs by quickly modifying existing products 3,52 0,95 0,709 3,893 -,096 -,893
We can modify products by adding new parts or substituting old parts easily 3,08 1,06 0,568 2,393 -,109 -,793
We can modify existing products quickly 3,11 1,00 0,654 2,805 ,078 -,877
We can modify existing products inexpensively 2,82 1,01 0,468 1,764 ,518 -,361
We can quickly introduce a new product into the market 2,94 1,03 0,735 2,266 ,123 -,675
We can take the lead in new product introduction - - - - - -
We can quickly substitute new products for those currently being produced 2,90 0,95 0,792 2,665 ,285 -,459
We can launch new products easily 2,93 0,91 0,923 2,881 -,055 -,931
We can launch new products inexpensively 2,67 1,01 0,742 2,411 ,553 -,096
We can operate efficiently at different levels of output 3,39 1,05 0,773 3,176 -,180 -1,032
We can operate profitably at different production volumes 3,27 1,01 0,722 3,158 -,054 -,736
We can economically run various batch sizes 3,40 0,97 0,746 4,135 -,312 -,439
We can quickly change the quantities for our products produced 3,76 1,07 0,650 2,887 -,799 -,160
We can vary aggregate output from one period to the next 3,73 0,83 0,690 2,982 -,789 ,913
We can easily change the production volume of a manufacturing process 3,60 0,97 0,495 1,828 -,660 ,339
We can produce a wide variety of products in our plants 3,90 1,00 0,718 3,636 -,791 ,008
We can produce different product types without major changeover 3,66 0,95 0,776 4,835 -,654 -,148
We can build different products in the same plants at the same time 3,99 0,86 0,845 4,987 -,912 1,126
We can produce, simultaneously or periodically, multiple products in a steady-state
operating mode
3,76 0,85 0,790 5,077 -,613 ,568
We can vary product combinations from one period to the next 4,01 0,88 0,810 5,044 -1,026 1,281
We can changeover quickly from one product to another 3,88 0,97 0,820 5,534 -,746 ,077
We pick and assemble multiple customer orders accurately and quickly at the finished
goods warehouse
3,75 1,00 0,822 17,157 -,523 -,401
We can provide multiple kinds of product packaging effectively at the finished goods
warehouse
3,49 1,03 0,626 4,771 -,294 -,562
We can use multiple transportation modes to meet schedule for deliveries 3,81 0,96 0,663 5,731 -,631 -,045
We can quickly and accurately label finished products 3,71 0,94 0,717 7,740 -,544 -,131
We have accurate records of quantities and locations of finished goods 4,11 0,88 0,829 14,480 -1,086 1,331
We can take different customer orders with accurate available-to-promise 3,96 0,86 0,645 4,816 -,750 ,194
We can quickly respond to multiple customers' delivery time requirements 3,71 0,88 0,619 3,947 -,839 1,089
We can effectively respond to multiple customers' requirements in terms of repair,
installation and maintenance of products
3,47 0,82 0,645 4,096 ,030 ,253
We can negotiate with customers in terms of prices and delivery time effectively
through long-term relationships
3,76 0,73 0,626 4,955 -,184 -,120
We involve customers to improve our services effectively 3,72 0,77 0,770 9,674 -,624 ,276
We quickly respond to feedback from retailers and consumers effectively 3,49 0,77 0,660 5,238 -,143 -,326
We continuously renew our competence to meet changing customer needs 3,69 0,73 0,780 11,548 -,966 1,818
We quickly take action based on all the information continuously collected along the
value chain
3,33 0,84 0,761 12,874 -,432 -,406
We continuously develop strategy based on maintaining a good relationship with our
major suppliers
3,52 0,79 0,592 4,229 -,601 ,368
We continuously experiment, learn, and improve our practices to improve productivity 3,78 0,64 0,668 5,547 -1,457 3,807
We quickly develop strategy based on the coordination and integration of information
along the value chain
3,19 0,76 0,727 9,999 -,339 -,208
We continuously experiment, learn, and improve our practices to improve customer
satisfaction
3,70 0,69 0,738 6,160 -,863 2,156
We have high customer retention rate (customers keep doing business with us) 4,20 0,82 0,683 6,120 -1,477 3,150
Customers are satisfied with ration of price and function of our products 3,80 0,64 0,764 7,185 -1,228 4,062
Customers perceive their money’s worth when the purchase our products 4,01 0,59 0,826 8,405 -1,430 7,610
Our customers are satisfied with the quality of our products 4,04 0,72 0,706 5,808 -1,046 3,181
Our firm has a good reputation for our products 4,31 0,70 0,815 10,828 -1,402 4,849
Strategy
development
flexibility
Customer
satisfaction
Product
modification
flexibility
New product
flexibility
Volume
flexibility
Mix flexibility
Physical
distribution
flexibility
Demand
management
flexibility
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Inspection of the individual item loadings presented in table 5 indicates that the sixth item of product
modification flexibility and volume flexibility (see the red letters in italics) have a load lower than 0,5
and a t-value less than 2 and should therefore be dropped. All remaining items with a loading higher
than 0,5 provide support for a high degree of individual item reliability (Hulland 1999; White et al.
2003). With a kurtosis value of 3,807 and 2,156 for the fourth and sixth item of strategy development
flexibility and kurtosis values higher than 1,99 for all the items of customer satisfaction, the measured
construct data deviate from normality.
The descriptive statistics for the constructs on a factor level are given in table 6. A visual overview of
each construct of flexibility and customer satisfaction is presented in the histograms in appendix 9.
Table 6: Descriptive statistics on factor level3
To determine the internal consistency of the hypothesized items to measure a single construct
Jöreskog’s (1971) measure of composite reliability is used (Fornell and Larcker 1981). The items
measuring the constructs can be considered as internally consistent if in all instances all composite
reliability values are higher than the 0,7 guideline suggested by Nunally and Bernstein (1994).
By inspection of each construct’s AVE the within-method convergent validity of all constructs used in
this study, with exception of demand management flexibility, are acceptable with a value above 0,50
(Garver and Mentzer 1999; Chin et al. 2003).
Discriminant validity can be determined by means of Fornell and Lacker’s (1981) test of the square
AVE. The square AVE calculated should exceed the correlation between the two respective
constructs. In this case all square AVE values are higher than the correlations mentioned and provide
evidence for sufficient discriminant and convergent validity (Chin 1998; Hulland 1999; Brown and
Chin 2004; Gefen and Straub 2005).
Product Modification
Flexibility
New Product
Flexibility
Volume Flexibility Mix Flexibility Physical Distribution
Flexibility
Demand Management
Flexibility
Strategy Development
Flexibility
Customer satisfaction
Product Modification
Flexibility
0,730
New Product Flexibility 0,385 0,802
Volume Flexibility 0,214 0,150 0,723
Mix Flexibility 0,345 0,266 0,466 0,794
Physical Distribution
Flexibility
0,052 0,218 0,254 0,403 0,722
Demand Management
Flexibility
0,209 0,209 0,234 0,321 0,309 0,666
Strategy Development
Flexibility
0,105 0,171 0,348 0,368 0,389 0,561 0,714
Customer satisfaction 0,270 0,106 0,373 0,401 0,419 0,451 0,599 0,761
Mean 3,225 2,861 3,524 3,867 3,805 3,631 3,534 4,072
St. Dev. 1,015 0,974 0,984 0,917 0,945 0,792 0,743 0,695
Composite reliability 0,848 0,877 0,844 0,911 0,866 0,799 0,861 0,872
Average variance
extracted (AVE)
0,533 0,643 0,522 0,631 0,521 0,444 0,510 0,579
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5.2 Hypothesis analyses
The results of the reliability analyses show good measurement property for all of the constructs with
exception of demand management which can be called sufficient. In this paragraph the hypothesis
between the six constructs of flexibility and customer satisfaction are tested.
In the first paragraph the correlation between each capability of flexibility and customer satisfaction is
presented. The path analyses are worked out in paragraph 5.2.2. At last the final results are given in
paragraph 5.2.3.
5.2.1 Correlation analyses
Each hypothesis (see chapter 3) describes a positive relationship between a single capability
dimension of supply chain flexibility and customer satisfaction. In the correlation matrix in table 7 a
complete overview of the correlations between all the dimensions of the research model is presented.
Notes: *p < 0,05 and **p < 0,01. N=83.
Table 7: Correlation matrix all dimensions research model
All capabilities have a positive and significant relationship with customer satisfaction, with exception
of the relationship between new product flexibility and customer satisfaction which is positive but not
significant. In appendix 10 the scatter diagrams and correlation matrices of each capability dimension
linked to customer satisfaction are given.
There is no multicollineairity between the dimensions in this matrix because no R-value is above 0,8
(Ten Hacken 2005; Ten Hacken et al. 2005).
3 All correlations are significant at the 0,05 level. Square root values of AVE (Average Variance Extracted) are given on the diagonal.
Product Modification
Flexibility
New Product Flexibility Volume Flexibility Mix Flexibility Physical Distribution
Flexibility
Demand Management
Flexibility
Strategy Development
Flexibility
Customer satisfaction
Pearson Correlation 1
Sig. (1-tailed)
Pearson Correlation 0,384**
1
Sig. (1-tailed) 0,000
Pearson Correlation 0,220*
0,128 1
Sig. (1-tailed) 0,023 0,125
Pearson Correlation 0,293
**0,227
*0,477
**1
Sig. (1-tailed) 0,004 0,019 0,000
Pearson Correlation 0,055 0,211*
0,257**
0,386**
1
Sig. (1-tailed) 0,310 0,028 0,009 0,000
Pearson Correlation 0,191*
0,178 0,250*
0,293**
0,279**
1
Sig. (1-tailed) 0,042 0,054 0,011 0,004 0,005
Pearson Correlation 0,089 0,159 0,350**
0,342**
0,341**
0,519**
1
Sig. (1-tailed) 0,213 0,075 0,001 0,001 0,001 0,000
Pearson Correlation 0,232*
0,080 0,347**
,377**
0,404**
0,448**
0,574**
1
Sig. (1-tailed) 0,017 0,236 0,001 0,000 0,000 0,000 0,000
Product Modification
Flexibility
New Product Flexibility
Physical Distribution
Flexibility
Mix Flexibility
Demand Management
Flexibility
Strategy Development
Flexibility
Customer satisfaction
Volume Flexibility
Page 42 of 91
5.2.2 Smart PLS
Smart PLS, a program to make PLS (Partial Least Squares) analyses, is used to test the research
model, because PLS is the most appropriate analyze technique for this study (Graber et al. 2002;
Haenlein and Kaplan 2004; O'Loughlin and Coenders 2004; Pirouz 2006):
• PLS uses no distributional assumptions and this is important because table 5 shows that the
distributions of the data deviate from normality (Fornell 1981; Chin 1995; Hulland 1999; Chin et
al. 2003).
• PLS is a very powerful tool that can be used in situations where the sample size is relatively small
in proportion to the parameters (Chin 1995; Cassel et al. 2000; Abdi 2003; Chin et al. 2003)
A PLS model assesses the measurement model and structural model at the same time, but uses two
stages in sequence for analyzing and interpreting the PLS model (Hulland 1999; White et al. 2003).
In the first stage reliability and validity are determined to assess the measurement model. After that
the structural- or inner model is evaluated in the second stage, where reliable and valid measures of
constructs are determined before conclusions about inter-construct relationships are drawn (Plouffe et
al. 2001).
The outcome of PLS is shown in figure 20 (see also appendix 11).
Figure 5: Research model tested using Smart PLS
Page 43 of 91
With a R² of 0,478, almost 48% of the variance in customer satisfaction can be explained by the six
capability dimensions of supply chain flexibility. The t-values are obtained via a bootstrap procedure
which consists of 500 runs (White et al. 2003).
Product Modification Flexibility has a significant positive impact on customer satisfaction (B = 0,208
and t = 2,543). New Product Flexibility has a small negative impact on customer satisfaction
(B = -0,139) and with a t value of 1,406 between -2 and 2 this is not significant. Volume and Mix
flexibility have a small positive influence on customer satisfaction (0,106 and 0,052) but both are not
significant with a t value of 1,121 and 0,573 (-2 < t < 2). Physical Distribution flexibility has a
significant positive influence on customer satisfaction (B = 0,200 and t = 2,741). The impact of
Demand Management flexibility on customer satisfaction is not significant (B = 0,105 and t = 0,925).
Last, Strategy Development Flexibility has a strong positive impact which is significant on customer
satisfaction ( B = 0,408 and t = 3,572).
5.2.3 Results
The empirical results of the model are presented in table 8.
Table 8: Results structural model
The results based on 83 respondents are not uniform. According to the correlations of each of the
flexibility capability dimensions on customer satisfaction nearly all dimensions have a positive
relationship with customer satisfaction, only the relationship between new product flexibility and
customer satisfaction is not significant. Regarding the effect of various flexibility capability
dimensions on customer satisfaction in the research model, the data support only three of the seven
relationships, namely hypotheses 1, 5 and 7 which are statistically significant with a t-value above 2
and a p-value less than 0,05. So product modification flexibility, physical distribution flexibility and
strategy development flexibility have a significant impact on customer satisfaction.
Relationship Coefficient t-value p-value Conclusion R²
Product Modification Flexibility => Customer Satisfaction 0,208 2,543 0,0064 H1 supported 0,478098
New Product Flexibility => Customer Satisfaction -0,139 1,406 0,0817 H2 not supported
Volume Flexibility => Customer Satisfaction 0,108 1,121 0,1328 H3 not supported
Mix Flexibility => Customer Satisfaction 0,052 0,573 0,2840 H4 not supported
Physical Distribution Flexibility => Customer Satisfaction 0,200 2,741 0,0038 H5 supported
Demand Management Flexibility => Customer Satisfaction 0,105 0,925 0,1789 H6 not supported
Strategy Development Flexibility => Customer Satisfaction 0,408 3,572 0,0003 H7 supported
Page 44 of 91
6. Discussion, conclusion, limitations and further research
In paragraph 6.1 in this chapter the results will be discussed. The conclusion and the managerial- and
theoretical implications are given in paragraph 6.2 In paragraph 6.3 the directions for further research
will be presented.
6.1 Discussion
In this study the impact of supply chain flexibility on customer satisfaction is determined by testing
the relationship between seven external or customer pleasing capabilities of supply chain flexibility
with customer satisfaction. The capabilities that are studied in this research are product modification-
and new product flexibility as part of product development flexibility, volume- and mix flexibility as
part of manufacturing flexibility, physical distribution- and demand management flexibility as part of
logistics flexibility and strategy development flexibility as part of spanning flexibility.
When these capabilities are tested in a direct one to one relationship with customer satisfaction the
results show that all capabilities have a significant positive relationship with customer satisfaction
with exception of new product flexibility which relationship was positive but not significant. These
results are nearly the same as in previous research on this topic by Zhang et al. (2002a; 2003; 2005;
2006) which shows a positive and significant relationship between product modification-, new
product-, volume-, mix-, physical distribution-, strategy development flexibility and customer
satisfaction. Testing the research model with all capabilities together with customer satisfaction, only
hypothesis 1, 5 and 7 are confirmed. So only product modification flexibility, physical distribution
flexibility and strategy development flexibility have a significant positive relationship with customer
satisfaction in testing this model. As there is no previous research on this topic in which a
comprehensive model with all flexibility capability dimensions in relation to customer satisfaction is
tested at once, no comparisons with model based results can be made using previous literature.
Maybe the results of the study of this model can be explained by:
• the direct visibility of these flexibility dimensions (Oke 2005) in relation to customer satisfaction
• the value of these dimensions from a mass customization perspective by which the customer
wants a individually customized product (product modification flexibility (Gerwin 1993; Koste
and Malhotra 1999; Petroni and Bevilacqua 2002) delivered in the right purpose on the right time
(physical distribution flexibility) (Das and Narasimhan 2000; Kumar et al. 2006) based on the
information given by the customer (strategy development flexibility) (Day 1994; Hart 1995;
Feitzinger and Lee 1997; Gilmore and Pine 1997; Beach et al. 2000; Da Silveira et al. 2001;
Stevenson and Spring 2007).
Page 45 of 91
Related to this:
• The reason why new product flexibility, which can also be important to foresee in the customers’
needs, has a less strong relationship with customer satisfaction can be found in the fact that new
products mostly have a relative long time to the market (Sanchez 1995; Gunasekaran et al. 2001;
Zhang and Doll 2001), the customer appreciates a recognizable product more than a new
(unknown) one (Olson et al. 1995) and more new product development expanding the product
variety does not always lead to satisfied customers (Gerwin 1993; Hart 1995; Gunasekaran et al.
2001; Zhang et al. 2009).
• Volume and mix flexibility are less visible. The customers see an end product delivered on time
and not the mechanism used to achieve this target. So the customers do not see mix- or volume-
flexibility, but it’s consequences, for instance in terms of delivery capability (De Toni and
Tonchia 1998; Jack and Raturi 2002; Olhager and West 2002; Oke 2005; Hallgren and Olhager
2009). Mix and volume flexibility are needed to reach the market at time and be efficient in this
process (Das and Narasimhan 2000). So volume and mix flexibility are not an end product on its
own from the perspective of the customer.
• The results with relation to impact of demand management flexibility on customer satisfaction
seem to be the same as the role of volume and mix flexibility. So the customers do not perceive
demand management flexibility, but they just recognize the consequences or results from the
activities executed to achieve demand management flexibility. Besides this, based on the findings
on the reliability analyses the construct of demand management is internally less consistent than
the other constructs used in this research. This does not correspond with the outcomes from
previous research by Zhang et al. (2005).
In short, customers does not care how an order is met, as long as it is met with a product that is
adjusted to fulfill their needs (Hart 1995; Oke 2005), a delivery in the right shape at the right time
based on the information the customer had given. According to the findings in this research to
improve customer satisfaction by delivering the right product at the right time based on the
information the customer had given, product modification-, physical distribution- and strategy
development flexibility are important and should therefore be stimulated to reach higher customer
satisfaction levels.
Page 46 of 91
6.2 Conclusion and implications
Companies are dealing with complex, continuous changing and uncertain environments due trends in
the area of globalization, technical changes and innovations and changes in the customers’ needs and
expectations. To cope with the increasingly uncertain and quickly changing environment firms strive
for flexibility.
In the literature, many definitions of flexibility are reported. The definition of flexibility used in this
thesis is:
“Flexibility is the organization’s ability to change or react to environmental uncertainty and to
meet the increasing variety of customer expectations without excessive costs, time and
organizational disruptions or performance losses”.
To achieve the level of flexibility that adds value to the customers, firms should look to flexibility
from a wider supply chain- or value chain perspective. Supply chain flexibility can be defined with
components of an intrafirm and interfirm level and includes product development flexibility,
production flexibility, logistics- and spanning flexibility. Each dimension of supply chain flexibility
can be divided into competences and capabilities. In this thesis only the influence of these external or
customer pleasing capabilities which directly add value in the customers eyes are studied to answer
the following research question:
“What is the impact of supply chain flexibility on customer satisfaction?”
The test of the relationship between the seven capabilities of flexibility and customer satisfaction
under 1000 manufacturing firms in the Netherlands with 100 employees or more using a questionnaire
filled in by managing general-, logistics- or manufacturing representatives lead to the following
conclusions:
• All capabilities of supply chain flexibility have a positive relationship on customer satisfaction
when every relationship is tested separately. Just the impact of new product flexibility on
customer satisfaction is not significant.
• Only product modification flexibility, physical distribution flexibility and strategy development
flexibility have a significant and positive impact on customer satisfaction when all flexibility
capabilities are tested together with customer satisfaction in one model.
Page 47 of 91
These results can possibly be explained by the visibility of the product modification-, physical
distribution and strategy development flexibility for the customer and the importance of these
flexibilities from a mass customization point of view.
The implications of this research for managers and academicians are described in paragraph 6.2.1 and
6.2.2.
6.2.1 Managerial implications
If managers are looking at flexibility they often look for a particular part of flexibility from a
particular managerial situation or problem (Upton 1994; Lau 1999; Chang et al. 2007). Previous
research on flexibility emphasizes the importance of a broad view of flexibility, where every
flexibility dimension is taken into consideration (Sethi and Sethi 1990; Slack 2005b, 2005a). Which
flexibility type is important depends on the situation. Each time a new decision must be made to
determine what is important under those specific circumstances. Often not the flexibility itself is the
most important thing in this decision, but the results that must be obtained (Narain et al. 2000; Oke
2005). So the situation and the chosen strategy influences the adoption of the flexibility dimensions
(Vokurka and O'Leary-Kelly 2000).
To determine which supply chain flexibility dimensions are important in relation with customer
satisfaction, only the customer facing or customer pleasing capabilities are part of this investigation.
These capabilities are tested separately with customer satisfaction, but also all together in one model.
The results of the tests between every flexibility capability (separately) and customer satisfaction in
this thesis are all positive, only the impact of new product flexibility on customer satisfaction is not
significant. This is nearly the same as the outcome of different studies of flexibility in relation to
customer satisfaction from Zhang et al. (2002a; 2003; 2005; 2006), where the results of all flexibility
capability dimensions on customer satisfaction are significant and positive. When testing all flexibility
capabilities in one model from a broad perspective on flexibility, only a few flexibility capabilities
seems to be important. The findings in this study are that out of seven flexibility capabilities in the
research model (see figure 4), only product modification flexibility, physical distribution flexibility
and strategy development flexibility have a significant positive impact on customer satisfaction. So to
satisfy a customer it is important to deliver the right modified product in the right purpose at the right
time, based on the information given by the customer.
These results show that the other flexibilities such as new product-, volume-, mix- and demand
management flexibility seems to be less important in relation to customer satisfaction. This does not
mean that these flexibilities should not be part of the model or consideration at all, because they could
Page 48 of 91
also be needed to support and fulfill the other for this specific situation more important flexibilities.
For instance delivery flexibility can hardly be achieved without any demand management-, volume-
and mix flexibility activities (De Toni and Tonchia 1998; Jack and Raturi 2002; Olhager and West
2002; Oke 2005; Hallgren and Olhager 2009).
In short the flexibility characteristics or dimensions used in this thesis to test this model are not new
(Lummus et al. 2003). Answering the research question (what is the impact of from supply chain
flexibility on customer satisfaction) only the dimensions of flexibility that are important from a
customer point of view are part of this investigation. In the same way managers need to understand
that depending on their situation and their own firm’s relationship with the entire supply chain they
must strive for the right selection of flexibility dimensions, to make a good choice to reach their
predetermined goal. This is important because not every flexibility dimension is equally related to a
specific firm performance measure and it is meaningless to develop a flexibility strategy which
increases flexibility but not reaches the goal (De Treville and Vanderhaeghe 2003; Sánchez and Pérez
Pérez 2005). Or like Golden and Powell (2000) describe it interpreting Suarez et al. (1992): “an
organization can be flexible in some way and less flexible in others”.
To obtain flexibility it is not sufficient to buy flexibility, it must be planned and managed according to
the changing circumstances to gain its benefits (Oke 2003; Boyle 2006). This is only possible from a
broad perspective on flexibility and when taking all important flexibility dimensions for that particular
situation together into consideration and not one at the time.
6.2.2 Theoretical implications
From the literature can be concluded that companies strive more and more for flexibility to cope with
the increasingly uncertain and quickly changing environment. An overview of the flexibility theory is
given describing the elements of flexibility, the perspectives on flexibility, the dimensions of
flexibility, the different aspects of flexibility and the comparison of flexibility and agility. A
definition of flexibility and supply chain flexibility is stated and to determine which supply chain
flexibilities are important in relation to customer satisfaction seven customer facing capabilities are
selected and admitted in a model, that can serve as a testable framework to relate supply chain
flexibility to customer satisfaction.
Next a questionnaire is used to gather information from 1000 manufacturing firms with one hundred
or more employees which are located in the Netherlands. The questionnaire was filled in by
managing general-, logistics- or manufacturing representatives.
Page 49 of 91
The empirical evidence supports the view suggested by the hypothesis 1 till 7 that all measured
flexibility capability dimensions have a positive relationship with customer satisfaction when tested
one at the time, only the impact of new product flexibility on customer satisfaction is not significant.
This is comparable with the results of different researches on this topic by Zhang et al. (2002a; 2003;
2005; 2006). When testing the comprehensive model only hypothesis 1, 5 and 7 are confirmed. Thus
only product modification flexibility, physical distribution flexibility and strategy development
flexibility have a significant positive impact on customer satisfaction.
These findings provide an interesting insight in the way the method of testing (solely or in a model
with other flexibility capability dimensions) lead to different results. From this point of view it is
extremely important that to consider which flexibility is important in a particular situation to reach a
particular predetermined goal, a broad view on flexibility and a model testing approach is used. In
practice a lot of different flexibility dimensions play a role together and not are not isolated, thus why
testing the relationship only one at the time.
6.3 Limitations and further research
Nevertheless there are also several limitations to this study and its generazability:
• The unit of analyses are manufacturing firms in the Netherlands with one hundred or more
employees;
• The information is conducted using long distance questionnaires, which rely on the interpretation
of the respondent and his/her view of the situation;
• A cross sectional study is used;
• Only the customer pleasing capabilities are part of the tested model;
• Each capability is tested by five or six items using a five point Likert scale;
• The questionnaire with constructs of supply chain flexibility and customer satisfaction is filled in
by managing general-, manufacturing- or logistics representatives which can cause bias.
Further research can be accomplished:
• Taking the supply chain as a unit of analyses instead of the firm perspective used in this study,
possibly by using semi-structured interviews;
• Using a longitudinal study to determine how it develops over time;
• Repeat the study for small and medium sized firms;
• Testing this model using a survey investigation under manufacturing firms and its customers at
the same time to prevent bias or repeat this test under the customers of manufacturing firms;
Page 50 of 91
• Repeat this study in one or more other countries to retest the investigated model and possibly
support it with more evidence;
• Although the statistical results show sufficient validity and reliability, the sample size and the
response rate should be increased in further studies to improve generalizability of the results;
• Using multiple informants to prevent the possible source of bias;
• Investigating the items of the demand management flexibility construct and look for
improvements of this construct;
• Focus on a model including internal competences and external capabilities of supply chain
flexibility in relation to customer satisfaction;
• Testing if there is a relationship between the different flexibility dimensions (competences and
capabilities).
Besides this it would be interesting to test the impact of supply chain flexibility dimensions on
customer satisfaction for a whole supply chain including at least a supplier, a manufacturer, a
distributor and a customer. With regard to the increasing importance of service activities in customer
oriented supply chains testing the model incorporating service flexibility dimensions or test this model
in service related firms or different segments would be a new challenge.
Page 51 of 91
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Appendix 1. Overview dimensions of manufacturing flexibility
Researchers Year Number of
dimensions of
flexibility
Dimensions of
flexibility
Definition
Gerwin 1987 /
1993
7 Mix flexibility The ability of a manufacturing process to produce
a number of different products at the same point in
time.
Changeover flexibility The ability of a process to deal with additions to
and subtractions from the mix over time.
Modification flexibility The ability of a process to make functional
changes in the product.
Volume flexibility The ease with which changes in the aggregate
amount of production of a manufacturing process
can be achieved.
Rerouting flexibility The degree to which the operating sequence
through which the parts flow can be changed.
Material flexibility The ability to handle uncontrollable variations in
the composition and dimensions of the parts being
processed. It also encompasses the ability to
handle more than one kind of substance either for
the same component of different components.
Sequencing flexibility The ability to rearrange the order in which
different kinds of parts are fed into the
manufacturing process.
Slack 1987 5 Product flexibility The ability to introduce novel products, or to
modify existing ones.
Volume flexibility The ability to change the level of aggregated
output.
Delivery flexibility The ability to change planned or assumed delivery
dates.
Mix flexibility The ability to change the range of products made
within a given time period.
Quality flexibility The ability to change planned product quality
levels
Browne et al. 1984 8 Machine flexibility The ease of change to process a given set of part
types.
Product flexibility The ability to change to process new part types.
Process flexibility The ability to produce a given set of part types.
Operation flexibility The ability to interchange ordering of operations
on a part.
Routing flexibility The ability to process a given set of parts on
alternative machines. In other words: the ability of
a flexible system to work in a suboptimal manner.
Volume flexibility The ability to operate profitable at varying overall
levels.
Expansion flexibility The ability to easily add capability or capacity.
Production flexibility The universe of part types that can be processed.
Page 61 of 91
Researchers Year Number of
dimensions of
flexibility
Dimensions of
flexibility
Definition
Sethi and
Sethi
1990 11 Machine flexibility The various types of operations that the machine
can perform without requiring a prohibitive effort
in switching form one operation to another.
Material handling
flexibility
The ability of a material handling system to move
different parts efficiently for proper positioning
and processing through the manufacturing facility
it serves.
Operation flexibility The ability of a part to be produced in different
ways.
Process flexibility The set of part types the system can produce
without major setups.
Product flexibility The ease with which new parts can be added or
substituted for existing parts.
Routing flexibility The ability of the manufacturing system to
produce a part by alternate routes through the
system.
Volume flexibility The ability of the manufacturing system to be
operated profitably at different overall output
levels.
Expansion flexibility The ease with which the capacity and capability of
a manufacturing system can be increased when
needed.
Program flexibility The ability of the system to run virtually untended
for a long enough period.
Production flexibility The universe of part types that the manufacturing
system can produce without adding major capital
equipment.
Market flexibility The ease with which the manufacturing system
can adapt to a changing market environment.
Vokurka and
O'Leary Kelly
2000 15 Machine flexibility Range of operations that a piece of equipment can
perform without incurring a major setup.
Material handling
flexibility
Capabilities of a material handling process to
move different parts throughout the manufacturing
system.
Operations flexibility Number of alternative processes or ways in which
a part can be produced within the system.
Automation flexibility Extent to which flexibility is housed in the
automation (computerization) of manufacturing
technologies.
Labor flexibility Range of tasks that an operator can perform within
the manufacturing system.
Process flexibility Number of different parts that can be produced
without incurring a major setup.
Routing flexibility Number of alternative paths a part can take
through the system in order to be completed.
Product flexibility Time it takes to add or substitute new parts into
the system.
Page 62 of 91
Researchers Year Number of
dimensions of
flexibility
Dimensions of
flexibility
Definition
Vokurka and
O'Leary Kelly
New design flexibility Speed at which products can be designed and
introduced into the system.
Delivery flexibility Ability of the system to respond to changes in
delivery requests.
Volume flexibility Rang of output levels that a firm can economically
produce products.
Expansion flexibility Ease at which capacity may be added to the
system.
Program flexibility Length of time the system can operate unattended.
Production flexibility Range of products the system can produce without
adding new equipment.
Market flexibility Ability of the manufacturing system to adapt to
changes in the market environment.
Koste and
Malhotra
1999 10 Machine flexibility The number and heterogeneity (variety) of
operations a machine can execute without
incurring high transition penalties or large changes
in performance outcomes.
Labor flexibility The number and heterogeneity (variety) of
tasks/operations a worker can execute without
incurring high transition penalties or large changes
in performance outcomes.
Material handling
flexibility
The number of existing paths between processing
centers ant the heterogeneity (variety) of material
which can be transported along those paths
without incurring high transition penalties or large
changes in performance outcomes.
Routing flexibility The number of products which have alternate
routes and the extent of variation among the routes
used without incurring high transition penalties or
large changes in performance outcomes.
Operation flexibility The number of products which have alternate
sequencing plans and the heterogeneity (variety)
of the plans used without incurring high transition
penalties or large changes in performance
outcomes.
Expansion flexibility The number and heterogeneity (variety) of
expansions which can be accommodated without
incurring high transition penalties or large changes
in performance outcomes.
Volume flexibility The extent of change and degree of fluctuation in
aggregate output level which the system can
accommodate without incurring high transition
penalties or large changes in performance
outcomes.
Mix flexibility The number and variety (heterogeneity) of
products which can be produced without incurring
high transition penalties or large changes in
performance outcomes.
Page 63 of 91
Researchers Year Number of
dimensions of
flexibility
Dimensions of
flexibility
Definition
Koste and
Malhotra
New product flexibility The number and heterogeneity (variety) of
products new which are introduced into production
without incurring high transition penalties or large
changes in performance outcomes.
Modification flexibility The number and heterogeneity (variety) of product
modifications which are accomplished without
incurring high transition penalties or large changes
in performance outcomes.
Narashim and
Das
2000 10 Equipment flexibility The ability of a machine to switch among different
types of operations without prohibitive effort.
Material flexibility The ability of equipment to handle variations in
key dimensional and metallurgical properties of
inputs.
Routing flexibility The ability to vary machine visitation sequences
for processing a part.
Material handling
flexibility
The ability of the material handling systems to
move material through the plant effectively.
Program flexibility The ability of equipment to run unattended for
long periods of time.
Mix flexibility The ability of a manufacturing system to switch
between different products in the product mix.
Volume flexibility The ability of a manufacturing system to vary
aggregate production volume economically.
Modification flexibility The ability of the manufacturing process to
customize products through minor design
modifications.
New product flexibility The ability of the manufacturing system to
introduce and manufacture new parts and
products.
Market/delivery
flexibility
The ability of the manufacturing system to
respond to or influence market changes.
Page 64 of 91
Appendix 2. Overview dimensions of supply chain flexibility
Researchers Year Number of
dimensions of
flexibility
Dimensions of
flexibility
Definition
Product flexibility The ability to handle difficult, non-standard orders to
meet special customer specifications and to produce
products characterized by numerous features,
options, sizes or colors.
Volume flexibility The ability to rapidly adjust capacity so as to
accelerate or decelerate production in response to
changes in customer demand.
Launch flexibility The ability to rapidly introduce large numbers of
product improvements/variations or completely new
products.
Access flexibility The ability to effectively provide widespread and/or
intensive distribution coverage.
Vickery et al.
1999 5
Responsiveness to
market(s)
The ability to respond to the needs and wants of the
firm's target market.
Operations system
flexibility
Ability to configure assets and operations to react to
emerging customer trends (product changes, volume,
mix) at each node of the supply chain.
Market flexibility Ability to mass customize and build close
relationships with customers, including designing
and modifying new and existing products.
Logistics flexibility Ability to cost effectively receive and deliver
products as sources of supply and customers change
(location changes, globalization, postponement).
Supply flexibility Ability to reconfigure the supply chain, altering the
supply of product in line with the customer demand.
Organizational flexibility The ability to align labor force skills to the needs of
the supply chain to meet customer service / demand
requirements.
Duclos et al.
2003 6
Information systems
flexibility
The ability to align information system architectures
and systems with the changing information needs of
the organization as it responds to changing customer
demand.
Operational systems
flexibility
The ability to reconfigure assets in line with
customer need, the ability to change processes as
demand changes and the ability to adjust capacity.
Lummes et al.
2003 5
Logistic processes
flexibility
The ability to adjust to global requirements, the
ability to serve distinct customer shipping
requirements, the ability to vary warehouse space,
the ability to vary transportation carriers and the
ability to introduce product postponement.
Page 65 of 91
Researchers Year Number of
dimensions of
flexibility
Dimensions of
flexibility
Definition
Supply network flexibility The ability to add and remove suppliers, the ability to
select suppliers who can add new products quickly,
the ability to vary supplier relationships and the
ability to have suppliers make volume changes.
Organizational design
flexibility
The organizational structure, the human resource
practices, the workforce capabilities, the ability to
form personal links with other nodes and the
company's culture at each node of the supply chain.
Lummes et al.
Information systems
flexibility
The ability to synchronize information systems with
supply chain partners, the ability to share information
across internal business processes and the ability to
pass information along the supply chain.
Product development
flexibility
The ability to introduce and launch new products and
modify existing products quickly and performance-
effectively.
Manufacturing flexibility The ability of the organization to manage
manufacturing resources and uncertainty to meet
various customer requests.
Logistics flexibility The ability of the organization to respond quickly to
customer needs in delivery, support and service.
Zhang
2002 4
Spanning flexibility The ability of the organization to provide horizontal
information connections across the supply chain.
Sourcing flexibility The ability of the purchasing function to respond in a
timely and cost-effective manner to changing
requirements of purchased components.
New product development
flexibility
The ability of the company to produce various new
designs in a timely cost-effective manner and to
flexibly deploy resources related to product
development.
Manufacturing/production
flexibility
The ability of the manufacturing system to produce
products of different types and different volume at an
acceptable speed and cost.
Punjawan
2004 4
Delivering flexibility The ability to change delivery dates, the ability of the
supply chain to deliver different types of products to
customers with a wide range of volume at an
acceptable cost and time.
Process flexibility The number of product types that can be
manufactured in each production site
Garavelli
2003 2
Logistics flexibility The different logistics strategies which can be
adopted either to release a product to the market or to
produce a component for a supplier.
Page 66 of 91
Appendix 3. The conceptual model of Supply Chain flexibility
Manufacturing flexibility
Logistics flexibility
Spanning flexibility
Competences Capabilities
Customer
satisfaction
Product concept
and Prototype
flexibility
Product development flexibility
Supply chain flexibility
Machine-, Labor-,
Materials
Handling- and
Routing flexibility
Product
modification- and
New product
flexibility
Volume- and
Mix flexibility
Physical supply-
and Purchasing
flexibility
Physical
distribution and
Demand Manag.
flexibility
Supply chain
information
dissemination
flexibility
Strategy
development
flexibility
Page 67 of 91
Appendix 4. Research model
Page 68 of 91
Appendix 5. Overview of SIC codes in sample
20: Manufacturing of food and kindred products
21: Tobacco manufacturing
22: Manufacturing of textile mill products
23: Manufacturing of apparel and other textile products
24: Manufacturing of lumber and wood products, except furniture
25: Manufacturing of furniture and fixtures
26: Manufacturing of paper and allied products
27: Printing, publishing and allied industries
28: Chemical manufacturing and allied products
29: Petroleum and coal products industries
30: Manufacturing of rubber and miscellaneous products
31: Manufacturing of leather and leather products
32: Manufacturing of stone clay, glass and concrete products
33: Primary metal industries
34: Manufacturing of fabricated metal products
35: Manufacturing of industrial and commercial machinery and computer equipment
36: Manufacturing of electrical equipment and components
37: Manufacturing of transportation equipment
38: Manufacturing of instruments and measurements equipment.
39: Miscellaneous manufacturing industries
Page 69 of 91
Appendix 6. Questions in survey
Product development flexibility
Product modification flexibility
1. We can quickly modify product design in response to customer requests
2. We can easily modify products to a specific customer need 3. We can better meet customer needs by quickly modifying existing products
4. We can modify products by adding new parts or substituting old parts easily
5. We can modify existing products quickly
6. We can modify existing products inexpensively
New product flexibility
1. We can quickly introduce a new product into the market
2. We can take the lead in new product introduction
3. We can quickly substitute new products for those currently being produced 4. We can launch new products easily
5. We can launch new products inexpensively
Manufacturing flexibility
Volume flexibility
1. We can operate efficiently at different levels of output
2. We can operate profitably at different production volumes
3. We can economically run various batch sizes
4. We can quickly change the quantities for our products produced
5. We can vary aggregate output from one period to the next
6. We can easily change the production volume of a manufacturing process
Mix flexibility
1. We can produce a wide variety of products in our plants
2. We can produce different product types without major changeover
3. We can build different products in the same plants at the same time
4. We can produce, simultaneously or periodically, multiple products in a steady-state
operating mode
5. We can vary product combinations from one period to the next
6. We can changeover quickly from one product to another
Logistics flexibility
Physical distribution flexibility
1. We pick and assemble multiple customer orders accurately and quickly at the finished goods warehouse
2. We can provide multiple kinds of product packaging effectively at the finished goods warehouse
3. We can use multiple transportation modes to meet schedule for deliveries
4. We can quickly and accurately label finished products
5. We have accurate records of quantities and locations of finished goods
6. We can take different customer orders with accurate available-to-promise
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Demand management flexibility
1. We can quickly respond to multiple customers' delivery time requirements
2. We can effectively respond to multiple customers' requirements in terms of
repair, installation and maintenance of products
3. We can negotiate with customers in terms of prices and delivery time effectively through long-term relationships
4. We involve customers to improve our services effectively
5. We quickly respond to feedback from retailers and consumers effectively
Spanning flexibility
Strategy development flexibility
1. We continuously renew our competence to meet changing customer needs
2. We quickly take action based on all the information continuously collected along the value chain
3. We continuously develop strategy based on maintaining a good relationship with our major
suppliers
4. We continuously experiment, learn, and improve our practices to improve productivity 5. We quickly develop strategy based on the coordination and integration of information along the
value chain
6. We continuously experiment, learn, and improve our practices to improve customer satisfaction
Customer Satisfaction
1. We have high customer retention rate (customers keep doing business with us)
2. Customers are satisfied with ration of price and function of our products 3. Customers perceive their money’s worth when the purchase our products
4. Our customers are satisfied with the quality of our products
5. Our firm has a good reputation for our products
Page 71 of 91
Appendix 7. Overview respondents other positions
Other positions: Respondents Percent
Business manager (customer service & supply chain) 1 1,25%
Chief Operations Officer 1 1,25%
Controller 1 1,25%
CS Representative 1 1,25%
Customer Operations Manager 1 1,25%
Customer service manager 1 1,25%
Customer support 1 1,25%
Director Supply Chain 1 1,25%
European Account Co-coordinator 1 1,25%
HR Manager 1 1,25%
HR-Quality Manager 1 1,25%
Improvement Manager 1 1,25%
Manager aftermarket 1 1,25%
Manager Customer Service & Supply Chain Relations 1 1,25%
Manager development and engineering 1 1,25%
Marketing Manager 4 5,00%
Material Manager 1 1,25%
Office manager 1 1,25%
Product Manager 1 1,25%
Purchasing Manager 3 3,75%
Sales intern department Manager 1 1,25%
Sales Manager 1 1,25%
Stafmedewerker Logistiek 1 1,25%
Supply Chain Director 1 1,25%
Supply Chain Manager 2 2,50%
Supply Chain Optimization Manager 1 1,25%
Supply Chain Specialist 1 1,25%
Technical Director 1 1,25%
Transport Manager 1 1,25%
Page 72 of 91
Appendix 8. Overview reliability analyses
Product Modification Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,820 0,820 6
Reliability Statistics
Mean Std. Deviation N
PM1 3,35 1,02 83
PM2 3,47 1,04 83
PM3 3,52 0,95 83
PM4 3,08 1,06 83
PM5 3,11 1,00 83
PM6 2,82 1,01 83
Item Statistics
PM1 PM2 PM3 PM4 PM5 PM6
PM1 1,000
PM2 0,684 1,000
PM3 0,364 0,391 1,000
PM4 0,277 0,317 0,534 1,000
PM5 0,466 0,384 0,388 0,554 1,000
PM6 0,275 0,278 0,413 0,502 0,657 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
PM1 16,000 14,220 0,556 0,529 0,797
PM2 15,880 14,132 0,551 0,497 0,799
PM3 15,831 14,508 0,564 0,367 0,796
PM4 16,265 13,758 0,589 0,444 0,791
PM5 16,241 13,527 0,679 0,567 0,771
PM6 16,530 14,130 0,573 0,477 0,794
Item-Total Statistics
The mean of the item loadings of product modification flexibility are all around the middle of the range (which is 3) of the scale from 1 to 5. With a crombach’s alpha of 0,820 which is higher as 0,8
the items of product modification flexibility are internally consistent and very good for basic research
(Nunally and Bernstein 1994; Baarda et al. 2007).
Page 73 of 91
New Product Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,749 0,750 5,000
Reliability Statistics
Mean Std. Deviation N
NP1 2,940 1,028 83
NP2 3,566 0,952 83
NP3 2,904 0,945 83
NP4 2,928 0,908 83
NP5 2,675 1,013 83
Item Statistics
NP1 NP2 NP3 NP4 NP5
NP1 1,00
NP2 0,16 1,00
NP3 0,50 0,17 1,00
NP4 0,60 0,16 0,55 1,00
NP5 0,46 -0,02 0,53 0,65 1,00
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
NP1 12,072 7,336 0,601 0,402 0,670
NP2 11,446 9,982 0,142 0,080 0,826
NP3 12,108 7,634 0,616 0,395 0,667
NP4 12,084 7,395 0,714 0,561 0,633
NP5 12,337 7,592 0,559 0,490 0,687
Item-Total Statistics
The mean of the item loadings of new product flexibility are all around the middle of the range (which
is 3) of the scale from 1 to 5. Item NP 2 is going to be dropped because of the negative and low
correlation loadings. With a crombach’s alpha of 0,749 which is higher as 0,7 the items of new
product flexibility can be considered as internally consistent (Nunally and Bernstein 1994; Baarda et
al. 2007).
Page 74 of 91
Volume Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,772 0,775 6
Reliability Statistics
Mean Std. Deviation N
VO1 3,386 1,046 83
VO2 3,265 1,013 83
VO3 3,398 0,975 83
VO4 3,759 1,066 83
VO5 3,735 0,828 83
VO6 3,602 0,975 83
Item Statistics
VO1 VO2 VO3 VO4 VO5 VO6
VO1 1,000
VO2 0,639 1,000
VO3 0,434 0,460 1,000
VO4 0,325 0,207 0,446 1,000
VO5 0,359 0,288 0,344 0,535 1,000
VO6 0,104 0,157 0,348 0,388 0,442 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
VO1 17,759 11,502 0,535 0,478 0,734
VO2 17,880 11,863 0,502 0,460 0,743
VO3 17,747 11,508 0,595 0,378 0,719
VO4 17,386 11,411 0,534 0,391 0,735
VO5 17,410 12,391 0,569 0,394 0,730
VO6 17,542 12,739 0,389 0,276 0,770
Item-Total Statistics
The mean of the item loadings of new product flexibility are all above the middle of the range (which
is 3) of the scale from 1 to 5. With a crombach’s alpha of 0,772 which is higher as 0,7 the items of
volume flexibility can be considered as internally consistent (Nunally and Bernstein 1994; Baarda et al. 2007).
Page 75 of 91
Mix Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,882 0,883 6
Reliability Statistics
Mean Std. Deviation N
MI1 3,904 0,995 83
MI2 3,663 0,954 83
MI3 3,988 0,862 83
MI4 3,759 0,850 83
MI5 4,012 0,876 83
MI6 3,880 0,968 83
Item Statistics
MI1 MI2 MI3 MI4 MI5 MI6
MI1 1,000
MI2 0,505 1,000
MI3 0,666 0,559 1,000
MI4 0,506 0,516 0,612 1,000
MI5 0,435 0,530 0,565 0,577 1,000
MI6 0,482 0,656 0,627 0,498 0,634 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
MI1 19,301 13,481 0,635 0,477 0,872
MI2 19,542 13,398 0,689 0,504 0,862
MI3 19,217 13,513 0,766 0,617 0,850
MI4 19,446 14,128 0,668 0,477 0,865
MI5 19,193 13,914 0,678 0,504 0,864
MI6 19,325 13,100 0,725 0,585 0,856
Item-Total Statistics
The mean of the item loadings of mix flexibility are all around 4 and above the middle of the range
(which is 3) of the scale from 1 to 5. With a crombach’s alpha of 0,882 which is higher as 0,8 the items of mix flexibility can be considered as internally consistent and very good for basic research
(Nunally and Bernstein 1994; Baarda et al. 2007)
Page 76 of 91
Physical Distribution Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,813 0,814 6
Reliability Statistics
Mean Std. Deviation N
PD1 3,747 0,998 83
PD2 3,494 1,029 83
PD3 3,807 0,956 83
PD4 3,711 0,944 83
PD5 4,108 0,884 83
PD6 3,964 0,862 83
Item Statistics
PD1 PD2 PD3 PD4 PD5 PD6
PD1 1,000
PD2 0,491 1,000
PD3 0,498 0,309 1,000
PD4 0,504 0,526 0,411 1,000
PD5 0,543 0,316 0,473 0,491 1,000
PD6 0,443 0,254 0,273 0,197 0,598 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
PD1 19,084 11,078 0,697 0,486 0,755
PD2 19,337 11,982 0,514 0,354 0,799
PD3 19,024 12,243 0,530 0,322 0,794
PD4 19,120 11,961 0,590 0,451 0,781
PD5 18,723 11,861 0,667 0,552 0,765
PD6 18,867 13,068 0,464 0,413 0,806
Item-Total Statistics
The mean of the item loadings of physical distribution flexibility are all around 4 and above the
middle of the range (which is 3) of the scale from 1 to 5. With a crombach’s alpha of 0,813 which is
higher as 0,8 the items of physical distribution flexibility can be considered as internally consistent
and very good for basic research (Nunally and Bernstein 1994; Baarda et al. 2007)
Page 77 of 91
Demand Management Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,690 0,691 5
Reliability Statistics
Mean Std. Deviation N
DM1 3,711 0,877 83
DM2 3,470 0,817 83
DM3 3,759 0,726 83
DM4 3,723 0,770 83
DM5 3,494 0,771 83
Item Statistics
DM1 DM2 DM3 DM4 DM5
DM1 1,000
DM2 0,448 1,000
DM3 0,157 0,337 1,000
DM4 0,259 0,268 0,381 1,000
DM5 0,268 0,363 0,150 0,459 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
DM1 14,446 4,713 0,408 0,226 0,660
DM2 14,687 4,535 0,528 0,324 0,603
DM3 14,398 5,316 0,357 0,216 0,675
DM4 14,434 4,785 0,492 0,322 0,621
DM5 14,663 4,909 0,449 0,285 0,639
Item-Total Statistics
The mean of the item loadings of demand management flexibility are all above the middle of the
range (which is 3) of the scale from 1 to 5. With a crombach’s alpha of 0,690 which is lower as 0,7
alpha is relatively low. According to table 10 no items can be deleted to raise the crombach’s alpha.
Because alpha is above 0,6 the items to measure demand management flexibility can be maintained (Nunally and Bernstein 1994; Baarda et al. 2007).
Page 78 of 91
Strategy Development Flexibility
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Case Processing Summary
Cases
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,806 0,806 6
Reliability Statistics
Mean Std. Deviation N
SD1 3,687 0,731 83
SD2 3,325 0,843 83
SD3 3,518 0,786 83
SD4 3,783 0,645 83
SD5 3,193 0,756 83
SD6 3,699 0,694 83
Item Statistics
SD1 SD2 SD3 SD4 SD5 SD6
SD1 1,000
SD2 0,524 1,000
SD3 0,243 0,368 1,000
SD4 0,501 0,378 0,296 1,000
SD5 0,397 0,570 0,363 0,287 1,000
SD6 0,581 0,441 0,401 0,234 0,554 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
SD1 17,518 7,180 0,619 0,529 0,763
SD2 17,880 6,644 0,642 0,454 0,757
SD3 17,687 7,584 0,447 0,252 0,803
SD4 17,422 8,052 0,457 0,317 0,797
SD5 18,012 7,110 0,610 0,448 0,765
SD6 17,506 7,326 0,621 0,510 0,764
Item-Total Statistics
The mean of the item loadings of strategy development flexibility are all above the middle of the
range (which is 3) of the scale from 1 to 5. With a crombach’s alpha of 0,806 which is higher as 0,8 the items of strategy development flexibility are internally consistent and very good for basic
research (Nunally and Bernstein 1994; Baarda et al. 2007).
Page 79 of 91
Customer Satisfaction
N %
Valid 83 100
Excludeda 0 0
Total 83 100
Cases
Case Processing Summary
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized
Items
N of Items
0,808 0,816 5
Reliability Statistics
Mean Std. Deviation N
CS1 4,205 0,823 83
CS2 3,795 0,639 83
CS3 4,012 0,595 83
CS4 4,036 0,723 83
CS5 4,313 0,697 83
Item Statistics
CS1 CS2 CS3 CS4 CS5
CS1 1,000
CS2 0,452 1,000
CS3 0,344 0,712 1,000
CS4 0,274 0,359 0,481 1,000
CS5 0,567 0,392 0,520 0,606 1,000
Inter-Item Correlation Matrix
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
CS1 16,157 4,451 0,515 0,414 0,803
CS2 16,566 4,810 0,606 0,566 0,768
CS3 16,349 4,840 0,659 0,599 0,756
CS4 16,325 4,710 0,536 0,417 0,788
CS5 16,048 4,388 0,699 0,577 0,737
Item-Total Statistics
The mean of the item loadings of customer satisfaction are all above the middle of the range (which is
3) of the scale from 1 to 5. With a crombach’s alpha of 0,808 which is higher as 0,8 the items of
customer satisfaction are internally consistent and very good for basic research (Nunally and
Bernstein 1994; Baarda et al. 2007)
Page 80 of 91
Appendix 9. Histograms flexibility capabilities and customer satisfaction
In the underneath figures, a histogram for each capability of flexibility and customer satisfaction is presented to get a visual overview of the responses.
Product modification flexibility
The responses for product modification flexibility are widespread between 11 and 30. Obvious is that there are no low scores between 5 and 11 and no high scores 28 and 29. The scores that most occurred
are 15, 18 and 24.
New product flexibility
Page 81 of 91
The response for the five questions for new product flexibility results in a wide spreading of the
scores between 8 and 24. The lowest scores of 5 till 8 and the highest score of 25 are not presented.
15 and 17 are the scores that have the highest frequencies.
Volume flexibility
The responses for volume flexibility are widespread between 6 and 30. Obvious is that there is only
one low score of 6 and all the others scores are spread around 20. The most appeared scores are 18
and 22.
Mix flexibility
The responses for mix flexibility are spread between 6 and 30. Obvious is that there is only one low
score of 6 and that most of the scores are relatively high and spread around 25, with 24 that most occurred.
Page 82 of 91
Physical distribution flexibility
The responses for physical distribution flexibility are widespread between 12 and 30. Obvious is that there are no low scores between 5 and 11 and the most appeared scores are 23 and 24.
Demand management flexibility
The response for the five questions for demand management flexibility results in a wide spreading of
the scores between 10 and 25. The lowest scores of 5 till 10 are not presented. The score with the
highest frequency is 19.
Page 83 of 91
Strategy development flexibility
The responses for strategy development flexibility are widespread between 15 and 27 with one
exception a low score of 6. The most occurred scores are 21 and 23.
Customer satisfaction
Remarkable in this histogram is that the scores are high and vary only between 15 and 25, with one
exception a score of 5. The scores for customer satisfaction (the dependant variable) are thus les widespread than the scores for the different flexibility capabilities (the independent variable). The
score that most occur is 20.
Page 84 of 91
Appendix 10. Correlation each flexibility capability dimension and
customer satisfaction
Product modification flexibility and customer satisfaction
Product
modification
flexibility
Customer
Satisfaction
Pearson Correlation 1 0,232*
Sig. (1-tailed) 0,02
Product modification flexibility
N 83 83
Pearson Correlation 0,232* 1
Sig. (1-tailed) 0,02
Customer Satisfaction
N 83 83
According to the correlation matrix and the scatter diagram there is a positive relationship (r =0,232
and p < 0,05) between product modification flexibility and customer satisfaction.
Page 85 of 91
New product flexibility and customer satisfaction
New Product
Flexibility
Customer
Satisfaction
Pearson Correlation 1 0,080
Sig. (1-tailed) 0,236
New Product Flexibility
N 83 83
Pearson Correlation 0,080 1
Sig. (1-tailed) 0,236
Customer Satisfaction
N 83,0 83
According to the correlation matrix and the scatter diagram there is a positive but not significant
relationship (r =0,080 and p > 0,05) between new product flexibility and customer satisfaction.
Page 86 of 91
Volume flexibility and customer satisfaction
Volume
flexibility
Customer
Satisfaction
Pearson Correlation 1 0,347**
Sig. (1-tailed) 0,001
Volume flexibility
N 83 83
Pearson Correlation 0,347** 1
Sig. (1-tailed) 0,001
Customer Satisfaction
N 83 83
According to the correlation matrix and the scatter diagram there is a positive relationship (r =0,347
and p < 0,05) between volume flexibility and customer satisfaction.
Page 87 of 91
Mix flexibility and customer satisfaction
Mix Flexibility Customer
Satisfaction
Pearson Correlation 1 0,377**
Sig. (1-tailed) 0,000
Mix Flexibility
N 83 83
Pearson Correlation 0,377** 1
Sig. (1-tailed) 0,000
Customer Satisfaction
N 83 83
According to the correlation matrix and the scatter diagram there is a positive relationship (r =0,377
and p < 0,05) between mix flexibility and customer satisfaction.
Page 88 of 91
Physical distribution flexibility and customer satisfaction
Physical
Distribution
Flexibility
Customer
Satisfaction
Pearson Correlation 1 0,404**
Sig. (1-tailed) 0,000
Physical Distribution Flexibility
N 83 83
Pearson Correlation 0,404** 1
Sig. (1-tailed) 0,000
Customer Satisfaction
N 83 83
According to the correlation matrix and the scatter diagram there is a positive relationship (r =0,404
and p < 0,05) between physical distribution flexibility and customer satisfaction.
Page 89 of 91
Demand management flexibility and customer satisfaction
Demand
Management
Flexibility
Customer
Satisfaction
Pearson Correlation 1 0,448**
Sig. (1-tailed) 0,000
Demand Management
Flexibility
N 83 83
Pearson Correlation 0,448** 1
Sig. (1-tailed) 0,000
Customer Satisfaction
N 83 83
According to the correlation matrix and the scatter diagram there is a positive relationship (r =0,448
and p < 0,05) between product modification flexibility and customer satisfaction.
Page 90 of 91
Strategy development flexibility and customer satisfaction
Strategy
development
flexibility
Customer
Satisfaction
Pearson Correlation 1 0,574**
Sig. (1-tailed) 0,000
Strategy development flexibility
N 83 83
Pearson Correlation 0,574** 1
Sig. (1-tailed) 0,000
Customer Satisfaction
N 83 83
According to the correlation matrix and the scatter diagram there is a positive relationship (r =0,574
and p < 0,05) between product modification flexibility and customer satisfaction.
Page 91 of 91
Appendix 11. Research model tested using Smart PLS