a theoretical framework for analyzing the dimensions of.pdf

Ž .Journal of Operations Management 18 1999 75–93www.elsevier.comrlocaterdsw

Conceptual note

A theoretical framework for analyzing the dimensions ofmanufacturing flexibility

Lori L. Koste a, Manoj K. Malhotra b,)

a Management Department, Seidman School of Business, Grand Valley State UniÕersity, Allendale, MI 49401, USAb Management Science Department, Darla Moore School of Business, UniÕersity of South Carolina, Columbia, SC 29208, USA

Received 13 April 1998; accepted 5 January 1999

Abstract

The competitive environment of today has generated an increased interest in flexibility as a response mechanism. Whilethe potential benefits of flexibility are familiar, the concept of flexibility itself is not well-understood. Neither practitionersnor academics agree upon, or know, how flexibility can be gauged or measured in its totality. Consequently, this study seeksto provide a framework for understanding this complex concept and to create a theoretical foundation for the development ofgeneralizable measures for manufacturing flexibility. With this objective in mind, we first critically examine diverse streams

Ž . Ž . Ž .of literature to define four constituent elements of flexibility: range-number R-N , range-heterogeneity R-H , mobility M ,Ž .and uniformity U . The R-H element is new, and has not been proposed before in prior literature. These four elements can

be applied to consistently define different types or dimensions of flexibility. Definitions for 10 flexibility dimensionspertaining to manufacturing are thus obtained. These definitions serve a dual purpose. First, they capture the domain offlexibility. Second, we show in this study how these definitions can be used to generate scale items, thereby facilitating thedevelopment of generalizable manufacturing flexibility measures. Several research avenues that can be explored once suchmeasures are developed are also highlighted. q 1999 Elsevier Science B.V. All rights reserved.

Keywords: Flexibility; Manufacturing; Scale development

1. Introduction

Global competition, rapidly changing technology,and shorter product life cycles have contributed tomaking the current manufacturing environment anextremely competitive one. Organizations face sig-nificant uncertainty and continuous change. Tradi-tional manufacturing approaches, such as mass pro-

) Corresponding author. Tel.: q1-803-777-2712; fax: q1-803-777-6876; e-mail: [email protected]

duction of a few standardized products, are no longersufficient competitive weapons by themselves. Cus-tomers are demanding a greater variety of high qual-

Ž .ity, low-cost goods and services Pine, 1993 . Orga-nizations must consequently develop new methodsand perspectives to meet these market needs in atimely and cost effective fashion.

Creating flexible organizations is one response todealing with such challenges. A firm that is flexibleand possesses a set of different strategic options canrespond effectively to dynamic environmentsŽ .Sanchez, 1995 . The competitive potential of flexi-

0272-6963r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.Ž .PII: S0272-6963 99 00010-8

( )L.L. Koste, M.K. MalhotrarJournal of Operations Management 18 1999 75–9376

bility at the organizational level is widely recognizedŽby managers Cox, 1989; De Meyer et al., 1989;.Upton, 1995b , leading many to proclaim flexibility

Žas the ‘next competitive battle’ De Meyer et al.,.1989 . Yet the concept itself is not well-understood.

Ž .Upton 1995b noted that ‘‘10 or 15 years ago,quality was much like flexibility is today: vague anddifficult to improve yet critical to competitiveness’’Ž .p. 75 .

One of the first steps in understanding and im-proving a manufacturing capability such as quality orflexibility is the ability to measure it. This need hasdriven researchers such as Swamidass and NewellŽ .1987 to measure it in aggregate, and others such as

Ž . Ž .Dixon 1992 , Suarez et al. 1995; 1996 , and UptonŽ .1997 to measure it for individual dimensions. Inspite of these efforts, a review by Gupta and GoyalŽ .1989 of the early measurement approaches con-cluded that a ‘‘single, all-encompassing measure of

Ž .manufacturing flexibility’’ p. 134 had yet to bedeveloped.

Such a conclusion prompted the development ofadditional measures, which were largely focused onthe numerous types of flexibility that appear in the

Žliterature e.g., Bernardo and Mohamed, 1992; Dixon,1992; Gupta and Somers, 1992; Suarez et al., 1995,

.1996; Upton, 1995a, 1997 . Unfortunately, these ef-Ž .forts have not proven sufficient. Gerwin 1993 reit-

erated the lack of ‘‘well-accepted operationaliza-tions’’ for flexibility measures. He also identifiedseveral voids in the literature, such as the need for amethod that can be used for ‘‘establishing a priori

Ž .the domain of flexibility’’ p. 400 , and developmentof flexibility measures that can span diverse industrygroups. A more recent literature review by De Toni

Ž .and Tonchia 1998 reaches the same conclusion.They state that ‘‘notwithstanding the importance andconstant interest raised by flexibility in academic andmanagerial circles, the measure of flexibility is still

Ž .an under-developed subject’’ p. 1605 .Further measurement efforts are thus needed.

Ž .Dixon 1992 developed measures for three flexibil-ity dimensions in the textile industry, while Suarez et

Ž .al. 1995; 1996 explored flexibility in the printedŽ .circuit board industry. Upton 1995a; 1997 focused

on a single type of flexibility in the fine paperindustry. Unfortunately, these measures cannot bewidely generalized to other industries.

Ž .Gupta and Somers 1992 developed the onlygeneralizable flexibility scale that has been publishedso far. They measured eleven types of flexibility.Table 1 contains the factor structure identified bythem and the number of items associated with eachfactor. Unfortunately, these scale items are ex-ploratory in nature and have very limited theoreticalbasis. Second, they lack a consistent set of defini-tions that can be uniformly applied to different typesof flexibility. Third, these scale items fail to capturethe entire domain of flexibility since they oftenaddress two elements simultaneously, thereby con-founding their measurement. Finally, seven of thenine flexibility dimensions were assessed with onlyone or two items, and not through multi-item mea-

Ž .sures as recommended by Churchill 1979 . Clearly,multi-item measures that are driven by theory, whichare generalizable to diverse industry groups, andwhich capture the entire domain of flexibility, stillneed to be developed.

The resolution of the two issues identified byŽ .Gerwin 1993 —establishing the domain of flexibil-

ity and developing generalizable measures, isparamount. Both issues can be addressed within thecontext of scale development, for which ChurchillŽ . Ž1979 provides a widely accepted paradigm see

.Fig. 1 . The initial step of this paradigm requires thespecification of the domain of the construct, which

Ž .Gerwin 1993 equated to the identification of thosedimensions of flexibility that should be included inmanufacturing flexibility. An alternative approach to

Ždomain specification followed by some authors e.g.,.Slack, 1983; Upton, 1994 is to identify elements, or

building blocks, which comprise flexibility. Theseelements are applicable to all types of flexibility

Table 1Ž .Factor structure identified by Gupta and Somers 1992

Flexibility dimension Number of items

Volume 1Programming 1Process 2Product and production 2Market 2Machine 2Routing 2Material handling 3Expansion and market 6

( )L.L. Koste, M.K. MalhotrarJournal of Operations Management 18 1999 75–93 77

Fig. 1. Churchill’s paradigm.

regardless of the firm or industry. Such an approachis thus fundamentally more generalizable. It can alsofacilitate better comparisons across studies, therebyproviding a necessary foundation for future research.

This study attempts to progress toward resolutionof these two issues by undertaking a comprehensivereview of the flexibility literature that spans diverse

manufacturing systems and research methodologies.This review leads to the identification of the domainof flexibility, which in turn provides a basis fordeveloping generalizable measures of flexibility. Wethen illustrate how such measures can be theoreti-cally developed and used for theory testing in thisfield of research.


2. Flexibility and its elements

Ž .Upton 1994 defined flexibility as ‘‘the ability tochange or react with little penalty in time, effort, cost

Ž .or performance’’ p. 73 . It has been widely recog-nized as a multi-dimensional concept within the

Žmanufacturing function e.g., Sethi and Sethi, 1990;.Hyun and Ahn, 1992; Gerwin, 1993 , and can be

either reactive or proactive in nature. The reactivenature of flexibility addresses the environmental un-certainty, both internal and external, faced by an

Ž .organization Slack, 1983 . The proactive nature offlexibility allows an organization to ‘redefine marketuncertainties’ or influence what ‘customers have

Žcome to expect from a particular industry’ Gerwin,.1993, pp. 396–397 . Flexibility can also refer to the

potential or actual flexibility of an organization.Actual flexibility is more easily assessed than poten-tial flexibility, as demonstrated by its frequent inclu-

Žsion in empirical research e.g., Dixon, 1992; Ettlieand Penner-Hahn, 1994; Suarez et al., 1995, 1996;

.Upton, 1995a .Flexibility is a relative attribute, as opposed to an

Ž .absolute one Tidd, 1991 . Flexibility is always ex-amined with respect to an alternative to assess itsmagnitude. Investment models consider the flexibil-ity of a plant or a machine with respect to another

Žplant or machine e.g., Fine and Li, 1988; Gaimon.and Singhal, 1992 . Similarly, empirical studies of

flexibility have sought to compare plants with com-Žpetitors in the same industry e.g., Jaikumar, 1986;

Dixon, 1992; Suarez et al., 1995, 1996; Upton, 1995a,.1997 . Finally, flexibility can be looked upon as ‘‘a

combination of factors like physical characteristics,operating policies, and management practices’’Ž .Gupta and Buzacott, 1989, p. 91 . Thus flexibility

Žmust be planned and managed Sethi and Sethi,.1990 . One can also view flexibility as the creation

of a capability that can exist in several differentforms throughout the organization.

We first formally state that the domain of flexibil-ity is comprised of different flexibility types ordimensions, with each dimension having its ownconstituent elements. This assertion is well-supportedby existing conceptual literature in the field, wherebythree elements of range, mobility, and uniformityhave been used to define any flexibility dimensionŽ .e.g., Slack, 1983, 1987; Upton, 1994 . However, the

empirical flexibility research indicates the need foran additional element. Consequently, we propose anddefend in this study that four elements should beused to define the dimensions of flexibility.

The first element of flexibility is range. BothŽ . Ž .Slack 1983 and Upton 1994 equated the range to

the number of different positions, or flexible options,that can be achieved for a given flexibility dimen-

Ž .sion. However, Upton 1994 also alluded to a differ-ent aspect of range—the extent of differentiationbetween the flexible options. We believe that thisaspect should be recognized as a separate rangeelement by itself, since the use of a numerical countalone may omit necessary information from a flexi-bility measure. To illustrate this point, consider plantsA and B. Plant A produces two different sedanmodels, while plant B produces a sedan and a mini-van. Both models in plant A use the same platform,while the sedan and minivan produced in plant Bhave uniquely different platforms and associated as-sembly lines. Thus relative to plant A, plant B willencounter different processing and material require-ments in manufacturing both a sedan and a minivan.If just the number of products is considered plants Aand B would be ranked as having the same range.However, if product heterogeneity were also takeninto account, plant B would be deemed as moreflexible. We recognize that the evaluation of hetero-geneity may not be as objective as a numericalcount, but both aspects are necessary to capture thefull extent of the range and create a richer measure-ment for it, as well as permit better comparisons ofrange across different industries.

In order to limit confusion, the following termi-nologies are used. The element that captures thenumber of viable options is labeled R-N, while theelement that captures the differences between op-tions is R-H. The exact nature of the relationshipbetween the two range elements is currently un-known, and likely to vary from one firm to another.

The third element that is used to define the di-Ž .mensions of flexibility is mobility Upton, 1994 .

Ž .Mobility M represents the ease with which theorganization moves from one state to another. Itcorresponds to the ‘ease of movement’ notion pro-

Ž .posed by Slack 1983; 1987 , who advocated the useof both time and cost to assess this element becauseof their inter-relatedness. In contrast, Gupta and


Table 2The elements of flexibility and potential indicators

Elements Indicators

Ž . Ž .Range-number R-N number of options operations, tasks, products, etc.

Ž . Ž .Range-heterogeneity R-H heterogeneity of options differences between operations, tasks, products, etc.

Ž .Mobility M transition penalties—time, cost, effort of transition

Ž .Uniformity U similarity of performance outcomes—quality, costs, time, etc.

Ž . Ž .Buzacott 1989 only considered the speed time ofŽ .the transition. Upton 1994 equated mobility with

the ability to incur small transition penalties withinthe range.

Transition penalties are not related to the costs toacquire or develop the range of flexibility. Theyrelate solely to movement within the range andassess the difficulties of exercising a flexible alterna-tive. They are incurred only when a flexible responseoccurs, and therefore can be considered transient. Acompany that incurs smaller transition penalties forsimilar gains in the number and heterogeneity ofoptions is deemed more flexible. Transition penalties

Ž .could include but are not limited to the time andcost of lost production, the scheduling effortsŽ .managerial time required to affect the transition, orthe scrap or rework that can be attributed to the

Žtransition Slack, 1983; Gerwin, 1987, 1993; Sethiand Sethi, 1990; Gupta and Somers, 1992; Upton,

.1994, 1995a . Thus, all transition penalties can beconverted into a surrogate form of time andror cost.Mobility improves as long as the tradeoffs between

Ž .time and cost Slack, 1987 are recognized, and thenet sum of transition penalties is reduced.

ŽThe final element of flexibility is uniformity Up-.ton, 1994 . This element has been omitted by Slack

Ž .1983; 1987 , but is recognized by Gupta and Buza-Ž . Ž .cott 1989 . Uniformity U captures the similarity of

performance outcomes within the range. The lessflexible system will exhibit peaks or valleys in per-

Ž .formance outcomes Upton, 1994 . These changes inŽ .performance are not one-time transient penalties.

Rather, they may affect production attributes for theentire duration of the flexible response. Uniformitycan be assessed through a large number of perfor-mance measures. These include, but are not limitedto, efficiency, productivity, quality, processing times,

Žor product costs e.g., Johnson and Kaplan, 1987;

Maskell, 1991; Bobrowski and Park, 1993; Upton,.1994 . These performance measures may be applica-

ble to more than one flexibility dimension, andresemble the competitive priorities of time, cost, andquality. Tradeoffs could exist between them, and netchanges must be evaluated to capture the conse-quences and desirability of a flexible response.

Thus far, the four elements that comprise thedomain of any flexibility dimension have been iden-tified and discussed, along with variables that can beused to assess each element. Table 2 summarizes theelements and their potential indicators, which wewill use to further understand different dimensions offlexibility.

3. The dimensions of flexibility

We conducted an exhaustive search and analysisof literature to map the four constituent elements tothe 10 most important and commonly cited flexibil-ity dimensions that pertain to the manufacturingfunction. Definitions provided by various researcherswere used to create this mapping, which is summa-rized in Table 3. It reveals several discernible trends.As expected, authors in the past have alluded onlyfaintly to the heterogeneity element, which we haveformally proposed for the first time in this study. Italso shows that the four elements have not beenequally addressed for most dimensions. This limita-tion is critical, since it fails to capture the entiredomain of flexibility, thereby inhibiting its completeunderstanding and measurement. Finally, only a frac-tion of the studies have focused on empirical obser-vations of actual industrial practice. Instead, priorwork in manufacturing flexibility has mostly beenconceptual or modeling based in its origin.

We used the insight created from such a mappinganalysis to propose a composite definition of each


Table 3Elements operationalized in prior flexibility research

Flexibility Flexibility elementsdimension Ž . Ž . Ž .Range-number R-N Range-heterogeneity Mobility M Uniformity U

Ž .R-H

w xw xw xw xw xw xw xw xw xw x w x w xw xw xw xw xw xw x w xw xw xw xw xw xw xMachine 3 4 8 10 11 12 14 16 25 26 11 3 8 10 11 12 14 26 4 14 16 25 36 37 42w xw xw xw xw xw xw xw xw x w xw xw xw x27 29 30 34 36 37 38 42 43 27 29 30 43

w xw xw xw xw xw xw xw xw x w xw xw xw xw xw xw x w xw xw xw xw xw xw xLabor 1 7 14 19 29 30 32 35 33 1 14 19 22 32 33 48 7 14 19 32 33 39 41w xw xw xw xw x w xw x34 39 41 42 48 42 48

w xw xw xw xw x w x w xw xMaterial 13 14 26 27 43 14 14 43handling

w xw xw xw xw xw xw xw x w xw xw xw xw x w xw x w xw xw xw xw xw xw xRouting 11 14 16 24 26 27 29 43 4 11 16 23 24 14 24 6 10 11 12 14 16 29

w xw xw xw x w xw xw xw xw xw xOperation 10 26 29 43 5 10 26 28 29 43

w xw xw xw xw x w xw xw xw xw x w xExpansion 10 11 14 29 43 10 14 26 27 43 14

w xw xw xw xw xw xw xw xw x w xw xw xw xw xw xw x w xw xw xw xw xw xw xVolume 2 10 14 15 18 21 24 26 27 14 23 24 29 42 44 45 2 10 14 26 27 29 42w xw xw xw xw xw xw xw x w xw xw x29 40 42 43 44 45 46 47 43 46 47

w xw xw xw xw xw xw xw xw xw xw x w xw xw xw xw x w xw xw xw xw xw x w xw xw xMix 2 8 9 10 11 16 17 18 20 23 26 11 16 24 46 47 2 11 17 20 24 44 8 11 31w xw xw xw xw xw xw xw xw xw x w xw x w xw x27 29 31 40 43 44 45 46 47 49 49 50 45 49

w xw xw xw xw xw xw xw xw x w x w xw xw xw xw xw xw x w xNew product 2 10 14 15 17 18 20 23 26 24 2 10 14 15 17 18 24 14w xw xw xw xw xw xw xw x w xw xw xw xw xw xw x27 29 40 43 44 45 46 47 26 27 40 43 44 45 47

w xw xw xw xw xw xw xw xw xw x w xw xw xw xw x w xModification 14 15 17 18 20 23 24 29 40 45 14 15 17 24 45 14

w x Ž . w x Ž . w x Ž . w x Ž . w xCitations: 1 Atkinson 1985 ; 2 Azzone and Bertele 1989 ; 3 Barad 1992 ; 4 Benjaafar 1994 ; 5 Benjaafar and RamakrishnanŽ . w x Ž . w x Ž . w x Ž . w x Ž . w x1996 ; 6 Bernardo and Mohamed 1992 ; 7 Bobrowski and Park 1993 ; 8 Boyer and Leong 1996 ; 9 Brennesholtz 1996 ; 10

Ž . w x Ž . w x Ž . w x Ž . w x Ž . w xBrowne et al. 1984 ; 11 Carter 1986 ; 12 Chandra and Tombak 1992 ; 13 Chatterjee et al. 1984 ; 14 Chen et al. 1992 ; 15 CoxŽ . w x Ž . w x Ž . w x Ž . w x Ž . w x1989 ; 16 Das and Nagendra 1993 ; 17 Dixon 1992 ; 18 Dixon et al. 1990 ; 19 Elvers and Treleven 1985 ; 20 Ettlie and

Ž . w x Ž . w x Ž . w x Ž . w x Ž . w x Ž .Penner-Hahn 1994 ; 21 Fiegenbaum and Karnani 1991 ; 22 Fryer 1974 ; 23 Gerwin 1987 ; 24 Gerwin 1993 ; 25 Gupta 1993 ;w x Ž . w x Ž . w x Ž . w x Ž . w x26 Gupta and Somers 1992 ; 27 Gupta and Somers 1996 ; 28 Hutchinson and Pflughoeft 1994 ; 29 Hyun and Ahn 1992 ; 30

Ž . w x Ž . w x Ž . w x Ž . w xJensen and Malhotra 1996 ; 31 Jordan and Graves 1995 ; 32 Kher and Malhotra 1994 ; 33 Malhotra and Kher 1994 ; 34 MalhotraŽ . w x Ž . w x Ž . w x Ž . w xand Ritzman 1990 ; 35 Malhotra et al. 1993 ; 36 Mandelbaum and Brill 1989 ; 37 Nagurar 1992 ; 38 Nandkeolyar and Christy

Ž . w x Ž . w x Ž . w x Ž . w x Ž . w x1992 ; 39 Nelson 1967 ; 40 Noble 1995 ; 41 Park and Bobrowski 1989 ; 42 Ramasesh and Jayakumar 1991 ; 43 Sethi and SethiŽ . w x Ž . w x Ž . w x Ž . w x Ž . w x Ž . w x1990 ; 44 Slack 1983 ; 45 Slack 1987 ; 46 Suarez et al. 1995 ; 47 Suarez et al. 1996 ; 48 Treleven and Elvers 1985 ; 49 UptonŽ . w x Ž .1995a ; 50 Upton 1997 .

Žflexibility dimension. These definitions shown in.Table 4 represent a consensus viewpoint, incorpo-

rate all four elements within its statement, and can beused as the basis for developing generalizable mea-sures of flexibility. We also outline below otherconsiderations that must be taken into account whenevaluating the content of the definition for eachflexibility dimension.

3.1. Machine flexibility

Machine flexibility is frequently included in bothŽconceptual and empirical flexibility literature e.g.,

.Carter, 1986; Boyer and Leong, 1996 , and is often a

key variable of study in the shop scheduling and dualŽ . Žresource constrained DRC literature e.g., Malhotra

.and Ritzman, 1990; Benjaafar, 1994 . The number ofŽdifferent operations not repetitions of the same op-

.eration a machine performs is the R-N element,while R-H captures the extent of differentiation be-tween operations.

Although prior research has considered the num-ber or variety of parts a machine produces as indica-

Žtors of the range e.g., Browne et al., 1984; Ra-.masesh and Jayakumar, 1991; Hyun and Ahn, 1992 ,

others have focused on the operations or tasks aŽmachine performs e.g., Carter, 1986; Mandelbaum


Table 4Definitions of flexibility dimensions

Dimension Definition

Ž .Machine flexibility the number and heterogeneity variety of operations a machine can execute without incurring hightransition penalties or large changes in performance outcomes

Ž .Labor flexibility the number and heterogeneity variety of tasksroperations a worker can execute without incurring hightransition penalties or large changes in performance outcomes

Ž .Material handling flexibility the number of existing paths between processing centers and the heterogeneity variety of material whichcan be transported along those paths without incurring high transition penalties or large changesin performance outcomes

Routing flexibility the number of products which have alternate routes and the extent of variation among the routes usedwithout incurring high transition penalties or large changes in performance outcomes

Ž .Operation flexibility the number of products which have alternate sequencing plans and the heterogeneity variety of the plansused without incurring high transition penalties or large changes in performance outcomes

Ž .Expansion flexibility the number and heterogeneity variety of expansions which can be accommodated without incurring hightransition penalties or large changes in performance outcomes

Volume flexibility the extent of change and the degree of fluctuation in aggregate output level which the system canaccommodate without incurring high transition penalties or large changes in performance outcomes

Ž .Mix flexibility the number and variety heterogeneity of products which can be produced without incurring high transitionpenalties or large changes in performance outcomes

Ž .New product flexibility the number and heterogeneity variety of new products which are introduced into production withoutincurring high transition penalties or large changes in performance outcomes

Ž .Modification flexibility the number and heterogeneity variety of product modifications which are accomplished without incurringhigh transition penalties or large changes in performance outcomes

and Brill, 1989; Sethi and Sethi, 1990; Barad, 1992;Chen et al., 1992; Nagurar, 1992; Gupta and Somers,

.1996 . We believe that the use of operations is moredescriptive of the range. Consider a CNC machinethat performs all operations for two parts. While thismachine processes only two parts, it may performnumerous operations on each part. In addition, theseoperations may be very dissimilar. The range ofmachine flexibility would thus be underestimated atthe part level.

The transition penalties for switching betweenoperations could include machine changeover time,machine setup cost, lost production time, or scrapattributed to the changeover. Changes in perfor-mance outcomes that result from machine choice,such as quality levels, costs, processing times, pro-ductivity levels, or efficiency levels, can be used toassess uniformity. Policies aimed at setup time re-duction can reduce transition penalties, while em-ployee participation in statistical process control

could assure consistent quality outcomes for differ-ent processing operations.

3.2. Labor flexibility

Labor flexibility has appeared extensively in theDRC literature, which has examined the effects ofrange, mobility, and uniformity on shop performanceŽe.g., Fryer, 1974; Bobrowski and Park, 1993; Kher

.and Malhotra, 1994 . The conceptual and empiricalflexibility literature is unfortunately remiss in ad-

Ž . Ždressing this dimension Chen et al., 1992 see.Table 3 . The presence of labor flexibility however

plays a vital role in most production processes andaffects performance.

The number and heterogeneity of tasks a workerperforms capture the range elements of labor flexibil-ity. The term ‘task’ may include either individualoperations or the operation of individual machines.The R-N element captures the extent of cross-train-


ing that occurs for different tasks, while R-H indi-cates the heterogeneity of the tasks. Organizationscan cross-train workers within a single department oracross departments. While the workers may be trained

Ž .to perform the same number of tasks R-N , theworkers who are trained across departments willlikely face a more diverse set of tasks and therefore

Ž .possess more heterogeneity. Mobility M could beassessed by the productive time lost in transferring

Ž .workers, and uniformity U by the extent to whichemployees achieve similar performance across dif-ferent tasks to which they are assigned.

Process choice and managerial policies can affectthe level of labor flexibility. Implementation of group

Ž .technology cells Hyer and Wemmerlov, 1984 orŽ .one worker multiple machines OWMM cells

Ž .Krajewski and Ritzman, 1996 can enhance thelevel of labor flexibility achieved. Managerial poli-cies on cross-training and appropriate reward struc-tures can reduce transition penalties and lead tomotivated employees who may be more consistent inwork methods.

3.3. Material handling flexibility

Table 3 shows that material handling flexibilityhas received less attention than other dimensions.The number of existing paths between processing

Ž .centers R-N , and the heterogeneity of materials thatŽ .the system can transport R-H , capture the range

elements of material handling flexibility. A flexiblesystem with high R-N would allow material to move

Žbetween a larger number of processing centers Chat-. Žterjee et al., 1984 , while a fixed system like a

.conveyor restricts material flow to designated pathsŽ .Coyle et al., 1996 . Similarly, a plant that canproduce two diverse products, like jet skis and snow-mobiles, using the same material handling systemŽ . Ž .Schonberger, 1986 is more flexible R-H than onethat requires separate material handling systems forthe two product lines. The proportion of multi-pur-pose pallets or fixtures that exist in the plant couldbe used to assess this element. The R-H element ofmaterial handling flexibility thus comes from havinga system that can effectively transport many differenttypes of material vs. another system that would berendered useless if changes occur in the packaging,

shape, density, or size of the material being trans-ported. Transition penalties could include the time orcost associated with adding or removing a path fromthe material handling system. Material transfer times,transfer costs, or the number of parts moved are allpossible performance outcomes that could be as-sessed.

The production system design and the processchoice primarily determine material handling fle-xibility. For example, a continuous flow systemrequires a capital intensive fixedrrigid materialhandling system that cannot be changed withoutconsiderable time and expense. For less restrictivesystems, managerial policy on the shop floor caninfluence this flexibility. Limited availability of

Ž .transfer equipment for cost purposes , or presenceof careless workers in a labor-intensive materialhandling system, could adversely affect transitionpenalties or performance outcomes.

3.4. Routing flexibility

Routing flexibility has been frequently studied inshop floor control and FMS scheduling literatureŽe.g., Azzone and Bertele, 1989; Bernardo and Mo-

.hamed, 1992; Benjaafar, 1994 . It relates to theability to use alternate processing centers, whichproves useful in the event of machine breakdowns oroverloads. The use of alternate routes changes thelocation at which processing occurs, but not theprocessing sequence of operations for a part.

The number of parts that have alternate routesŽ . Ž .R-N , and the extent R-H to which a route can be

Ž .varied e.g., Gerwin, 1987, 1993 , capture the rangeelements of routing flexibility. The R-H elementreflects the average number of alternate machines towhich a processing operation can be routed, aver-aged across all parts in the system. For example,consider part Y that requires four processing opera-

Ž .tions A–B–C–D on machines 1–3–5–7. A sce-nario where only operations B and D could be routedto alternate machines has less R-H than the casewhere all four operations have an alternate machine.

ŽA greater variety of routing options more alternate.machines per processing operation will increase the

complexity involved in rerouting parts. The time orcosts expended to facilitate a route change are poten-tial transition penalties, while uniformity can be


assessed through differences in processing times orquality that occur with the use of an alternate route.

3.5. Operation flexibility

ŽOperation flexibility sometimes termed sequenc-.ing flexibility appears in both shop floor scheduling

Žas well as the flexibility literature e.g., Hutchinsonand Pflughoeft, 1994; Benjaafar and Ramakrishnan,

.1996 , and is an attribute of a part, not a productionŽ .process Sethi and Sethi, 1990 . Its existence relies

primarily on the development of multiple processingplans, without which it is not an option. Care mustbe taken to differentiate between operation and rout-ing flexibility. Operation flexibility involves chang-ing the actual sequence of operations performed,while routing flexibility changes the machines thatdo the processing for a given sequence of operations.Thus part design largely determines operation flexi-bility, while routing flexibility arises primarily frommanufacturing system design and the presence offlexible machines that can process several differentoperations.

The number of parts that have alternate-processingŽ .plans R-N , and the heterogeneity of the processing

Ž .sequences R-H , capture the range elements of oper-ation flexibility. The R-H element reflects the extent

Ž .of permutation positional changes in processingsequences averaged across all parts in the system.For example, the normal processing sequence for a

Ž .part plan 1 could be to perform operations 1–2–3–Ž .4. The first alternate plan plan 2 may be to reverse

Ž .the first two operations 2–1–3–4 . This would al-low shifting work off machine 1, which may be atemporary bottleneck, to machine 2. A second alter-

Ž .nate plan plan 3 could permute the entire sequenceŽ . Ž .3–1–4–2 . Thus plan 3 is more heterogeneous R-Hsince it represents four positional changes comparedto only two such changes in alternate plan 2. Thepositional changes of operations are important be-cause they may change the physical configuration ofthe part, and create additional complexity as R-Hincreases. The time or costs incurred to switch to analternate-processing plan constitute transition penal-ties. These costs may also include any additionalsetup costs that are incurred when operation se-quences are changed. Quality levels or product costs

are likely performance outcomes that could be moni-tored for changes.

3.6. Expansion flexibility

Table 3 shows that expansion flexibility has re-ceived limited empirical attention. It relates to in-

Ž . Žcreasing the capacity e.g., output or capability e.g.,. Žquality or technological state of the system Sethi

.and Sethi, 1990 . In contrast to the other flexibilitydimensions discussed, expansion flexibility is notconfined to the resources currently available in theproduction system. Additional machines can be pur-

Ž .chased, labor added extra employees or shifts , ornew technology incorporated.

The number and variety of expansions that can beaccommodated capture the range elements of expan-sion flexibility. The number of possible expansionsŽ .R-N is limited by the extent of restrictive linkagesbetween equipment and modules. Severely restrictedinformation system linkages or material handlingpaths would hinder expansion flexibility. The second

Ž .range element R-H provides insight into systemdesign and organizational capabilities. Consider Cha-parral Steel, which was able to increase capacityfrom 250,000 to 500,000 tonsryear by using ‘‘largerladles and transformers, computers, and more effi-

Žcient operating procedures’’ Krajewski and Ritz-.man, 1996, p. 60 . The latter two improved the

capability of the production process, while the largerladles increased system capacity. Transition penaltiescould include the time required to add the newproduction components and restart the productionsystem, while quality levels, product costs, and pro-ductivity are potential performance indicators.

3.7. Volume flexibility

Volume flexibility allows organizations to re-spond quickly and efficiently to both increases anddecreases in aggregate demand levels. The conceptof volume flexibility evolved from the economics

Ž .literature e.g., Knight, 1928; Stigler, 1939 andappears frequently in both conceptual and empirical

Žflexibility research e.g., Cox, 1989; Fiegenbaum andKarnani, 1991; Chen et al., 1992; Gupta and Somers,

. Ž .1992 . Carlsson 1989 notes that volume flexibilityhas traditionally been discussed ‘‘in terms of firms’


cost curves: flexibility varies inversely with the cur-vature of total costs. If the average total cost curve isU-shaped, the more flat-bottomed it is and the moreslowly marginal cost rises, the greater is the firm’s

Ž .flexibility’’ p. 181 . Cost curves in Fig. 2 illustratethat firm A is more flexible than firm B. Volumeflexibility only addresses short-term changes withinthe flat area of the curve. Larger, long-term changesin aggregate volume that lie outside this region maybe economically infeasible with the existing coststructure of the firm. Thus to capture the true level ofvolume flexibility, the system must be constrained to

Ž .its current configuration. Bylinsky 1983 providesan example of a volume flexible machine tool plantthat consists of 65 computer-controlled machines, 34robots, and 215 workers. The production capacity ofthe automated plant is equivalent to a conventionalfactory employing 2500 people. The plant has amaximum capacity of US $230 million in revenue,but can reduce volume to US $80 millionryearwithout laying off workers.

The levels of aggregate output that the manufac-turing system can achieve indicate the first range

Ž .element R-N . In assessing the range, the effectivecapacity of the system that can economically besustained under normal conditions must be consid-

Žered instead of the design or peak capacity Krajew-.ski and Ritzman, 1996, p. 277 . The upper and lower

end points of the range that bracket the flat area ofthe curve in Fig. 2 indicate the feasible volumes atwhich the firm can still operate profitably. The het-

Ž .erogeneity of volume changes R-H indicateswhether changes in aggregate volume can be at-

Fig. 2. Relationship between cost and volume.

tributed to a few products only or the entire productŽ .line. For a similar change in aggregate output R-N ,

an organization that can change production volumefor only a single part is less flexible with respect toŽ .R-H than a second organization that can changeproduction volume for its entire product line. Thetime required to change the output level is a possibletransition penalty. Performance outcomes for volumeflexibility could include production costs, qualitylevels, or system profitability.

3.8. Mix flexibility

Mix flexibility, or process flexibility, has receivedŽ .substantial exposure in the literature see Table 3 .

Although the term ‘process flexibility’ has beenŽwidely used e.g., Browne et al., 1984; Sethi and

.Sethi, 1990; Boyer and Leong, 1996 , recent re-search has begun to use the term ‘mix flexibility’Že.g., Dixon, 1992; Hyun and Ahn, 1992; Suarez et

.al., 1995, 1996 . This latter terminology is moredescriptive, and in an effort to reduce confusion, isadopted here. The level of mix flexibility in anorganization must be assessed within the currentproduction system configuration without considering

Žmajor setups or facility modifications Dixon, 1992;.Gupta and Somers, 1992 . Otherwise, an organiza-

tion could acquire additional resources to meetchanges in demand. This is not a flexible approach,just a capital intensive one. Mix flexibility facilitatesa broad product line that improves a firm’s competi-

Ž .tive position Kekre and Srinivasan, 1990 .Ž .The number of products R-N provides a strict

numerical count of the end items manufactured by anŽ .organization. The heterogeneity of products R-H

provides a broader insight into the range of mixflexibility, and has proven empirically useful in as-

Žsessing this dimension e.g., Suarez et al., 1995;.Upton, 1997 . Consider a firm that produces 30 very

similar products and another one that produces 30products that are very different from one another.Although R-N is identical, the latter firm requires ahigher degree of skill and expertise to create itsproduct mix, and is thus more flexible with respectto R-H. Transition penalties could include the timeor cost required to change the product mix, whileproductivity or quality levels are possible perfor-mance indicators.


3.9. Product flexibility

Product flexibility has been equated with the in-Žtroduction of new products e.g., Azzone and Bertele,

.1989; Chen et al., 1992 or, more broadly, with boththe introduction of new products and the modifica-

Žtion of existing ones e.g., Slack, 1987; Cox, 1989;.Hyun and Ahn, 1992; Ettlie and Penner-Hahn, 1994 .

The organizational skills and abilities required tointroduce new products may be significantly differ-ent from those required to modify existing onesŽ .Olson et al., 1995 . Consequently, we propose thatproduct flexibility should be addressed via two dis-tinctly different dimensions—new product flexibilityand modification flexibility.

3.9.1. New product flexibilityA product is considered new if ‘‘its functional

characteristics differed, i.e., its end use was not thesame, from those of any other product made previ-

Ž .ously by the plant’’ Dixon, 1992, p. 134 . Thenumber and variety of new products introduced byan organization capture the range elements of newproduct flexibility. The number of new productsŽ .R-N provides insight into an organization’s strate-gic emphasis on product development, while hetero-

Ž .geneity R-H relates to the innovativeness of theproducts. This element thus differentiates betweeninnovation and status quo product development inwhich the new products are all fairly similar to one

Žanother see Kleinschmidt and Cooper, 1991; Calan-.tone et al., 1995; Olson et al., 1995 . Development

time or costs incurred in creating a new productcould represent transition penalties. The similarity ofperformance outcomes is captured through produc-tivity or quality changes that occur with new productintroduction.

Since early or on-time introduction of a newproduct can significantly impact its profitabilityŽ .Vesey, 1991 , organizations are striving to be con-sistent in their product development efforts. Someorganizations, like Rubbermaid and 3M, have evenset requirements for the percentage of sales at-tributed to products introduced in the preceding 5

Ž .years Milgrom and Roberts, 1990 . Such a directiveensures a continuous stream of new products and anability to stay ahead of competitors.

3.9.2. Modification flexibilityModification flexibility addresses those product

changes that are less involved than the developmentof an entirely new product. They may often bedriven by customer requests. A product is consideredmodified if ‘‘its functional characteristics were main-tained’’, but other aspects of the product ‘‘were

Žchanged to better satisfy a customer’s needs’’ Di-.xon, 1992, p. 134 . These modifications can be of

several types. An existing design can be modified fora particular customer. Modifications also encompassextensions of the product line with an improvedproduct design or feature. These types of changesmay often be accomplished through engineeringchange orders, and thus tend to be less chaotic on aproduction system than the introduction of an en-tirely new product.

The number of modified products developed andthe heterogeneity of the modifications capture therange elements of modification flexibility. The num-

Ž .ber of modified products R-N indicates the cus-tomer responsiveness achieved by the organization.

Ž .The heterogeneity of the modified products R-Hcaptures the span of skills and abilities exercised bythe firm in creating that flexible customer response.While the simultaneous modification of several fea-tures of a product may be harder than modification

Ž .of one feature R-H , the ability to customize prod-ucts may provide several competitive advantagessuch as charging premium prices and entering smallniche markets that would otherwise be unprofitable.Transition penalties could include the time or costexpended to make the modification. Productivity andquality are likely performance indicators.

4. The flexibility hierarchy

Thus far, 10 manufacturing flexibility dimensionshave been discussed on an individual basis, with noconsideration for the relationships, if any, that mayexist between them. These potential relationships,however, must be investigated if flexibility is to beunderstood in a holistic fashion in manufacturingorganizations. Even though several hierarchies basedon such relationships between flexibility dimensions

Žhave been developed in prior literature e.g., Browneet al., 1984; Sethi and Sethi, 1990; Hyun and Ahn,


Tab

le5

Rel

atio

nshi

psbe

twee

ndi

men

sion

sof

flex

ibili

ty


.1992 , decisions about which dimensions should beallocated to different tiers are still being debated. Inorder to further this debate, we identified thosestudies that discussed any relationship between flexi-bility dimensions. Out of the 18 studies that met thiscriterion, only four were empirical studies, while theremaining 14 were conceptual in nature.

Ž .The relationships or linkages between the di-mensions of flexibility contained in these 18 studiesare summarized in Table 5. The row and column

headings in Table 5 represent the flexibility dimen-sions. For the row headings, the number in parenthe-

Ž.ses corresponds to the number of articles thatdiscuss the paired or immediate hierarchical relation-ship between that flexibility dimension and the re-maining flexibility dimensions listed in the columns.For example, machine flexibility is identified as anecessary building block for other flexibility dimen-sions in 12 studies. The matrix cells identify those

Žwx.citations which proposed or supported that rela-

Fig. 3. Hierarchy of flexibility dimensions.


tionship. The number at the bottom of the cell identi-fies the number of citations per cell. Thus, eventhough machine flexibility is thought to support rout-ing flexibility, only one study out of 12 studiespertaining to machine flexibility discussed this rela-

Ž .tionship Sethi and Sethi, 1990 . In contrast, machineflexibility is widely regarded as a requirement for the

Ž .development of mix flexibility 10 out of 12 studies .Thus the matrix in Table 5 can also be used toevaluate the degree of consensus that exists amongprior researchers for the presence or absence of animmediate hierarchical relationship among differentflexibility dimensions.

An examination of the matrix in Table 5 revealsseveral interesting trends. There are five flexibility

Ždimensions expansion, volume, mix, new product,.and modification which do not have any entries in

their matrix rows. It appears that these dimensionsdo not support the development of the other dimen-sions in the matrix. Consequently, they are probablyhigher-level flexibility dimensions. There are also

Žthree flexibility dimensions machine, labor and ma-.terial handling which do not have any entries in

their matrix columns. They appear not to rely on anyother flexibility dimensions. This suggests that theyare the lowest level flexibility dimensions amongthose considered, and thus mostly serve as the build-ing blocks for higher level dimensions.

Hierarchical relationships between machine flexi-bility, labor flexibility, and material handling flexi-bility are not proposed. These dimensions then mustexist at a similar hierarchical tier. Similarly, expan-sion flexibility, volume flexibility, mix flexibility,new product flexibility, and modification flexibilityhave no proposed hierarchical relationships betweenthem. These dimensions then must also be at thesame tier. Finally, the majority of support is for ahierarchical relationship between the first three andthe last five flexibility dimensions.

These trends were used by us to propose a newflexibility dimension hierarchy shown in Fig. 3. Con-sistent with the linkages presented above, the lowertiers contain those flexibility dimensions that serveas building blocks for the flexibility dimensions inthe upper tiers. The lower tier dimensions also tendto be more tactical, while those in the upper tiers ofthe hierarchy tend to be more strategic. Finally, eventhough each tier of the hierarchy contains several

flexibility dimensions, the nature of lateral relation-ships between dimensions at any given tier is notbeing discussed. In the absence of any empiricalevidence, the nature and presence of those relation-ships cannot be ascertained.

The schematic representation of Fig. 3 has alsobeen influenced by the work of Hyun and AhnŽ .1992 , with several key differences. Our hierarchyreflects a greater number of manufacturing relatedflexibility dimensions. In addition, the literature baseused to develop the hierarchy is more current andreferences studies from several diverse research

Ž .streams. Hyun and Ahn 1992 portray only threetiers of flexibility, while the hierarchy in Fig. 3consists of five tiers in the organization. Finally,

Ž .Hyun and Ahn 1992 illustrate an inverted cone asopposed to an upright one. The cone shape helpsportray flexibility as a capability. As an organizationprogresses in the development of flexibility, i.e.,moves up the cone, its capabilities with respect toflexibility increase.

5. Conclusions and future research directions

Our discussion has focused on defining 10 dimen-sions of manufacturing flexibility and their con-stituent elements. A hierarchy that proposes relation-ships between these flexibility dimensions was alsooutlined. The identification of four elements, andtheir use in defining the 10 dimensions of flexibility,specifies the domain of flexibility. Thus, the first

Ž .step in the paradigm of Churchill 1979 is satisfied.The remaining steps of the paradigm can then beundertaken, and the existence of these four elementsverified for all 10 flexibility dimensions.

While the completion of the paradigm is beyondthe scope of this study, we provide an illustration ofits continuation as applied to the machine flexibilitydimension. The domain of this construct is satisfiedby the definition contained in Table 4. The second

Ž .step see Fig. 1 involves conducting a literaturesearch, interviews, or case studies to generate sampleitems. These items should possess content validity.The literature search for this study summarized inTable 3 revealed a number of items that address theindividual elements of machine flexibility. As ad-

Ž .vised by Churchill 1979 , we requested a group of


academics and practitioners to evaluate these ma-chine flexibility items and assess their ability tocapture this construct. The feedback thus obtainedwas used for the initial purification of the items. Theresulting items are reported in Table 6. These itemscan then be advanced to the next stage and theparadigm completed. Thus, the framework proposedin this study appears to be a promising approach fordeveloping psychometrically sound measures forother flexibility dimensions.

Several avenues for future research open up oncepsychometrically sound, generalizable measures aredeveloped for different dimensions of flexibility. Weoffer six potentially useful research avenues thatpivot on measuring the impact of flexibility on orga-nizational performance.

Ž .1 Theories of managerial decision making andthe effectiveness of flexibility at the organizationallevel should be proposed and tested. For example,strategic decisions may guide the development ofcertain flexibility dimensions or incorporate thosethat already exist in an organization. The alignmentof these flexibility dimensions with the competitiveenvironment of the organization could be examinedand performance ramifications analyzed.

Ž .2 Studies that are focused on empirically testingthe nature of the hierarchical and lateral relationshipsbetween the flexibility dimensions as proposed inFig. 3 should be undertaken. Within this context,researchers could seek to understand the nature oftradeoffs that may exist between different dimen-sions of flexibility. For example, are organizationsmore likely to favor and promote one type of flexi-bility over another? Is there synergy between certaindimensions of flexibility? What degree of flexibilityprovides a sufficient building block for the formationof higher-level flexibility dimensions? Do increasesin the level of those flexibility dimensions that serveas building blocks lead to similar gains in higher-levelflexibility dimensions? Are the relationships betweendifferent dimensions of flexibility strong, moderate,or weak?

Ž .3 The various models of flexibility given byŽ .prior authors should be tested. Beckman 1990 pro-

poses that flexibility in manufacturing ‘‘must bedeveloped in response to some well-defined strategic

Ž .need’’ p. 114 . This implies flexibility is strictlyreactive in nature. This proposition should be tested

and the competitive strength or value of flexibilitymore fully explored and understood. Hyun and AhnŽ .1992 propose that certain dimensions of flexibilityare ‘static’ or unchanging, while others are ‘dy-namic’. Longitudinal studies could measure specificflexibility dimensions in firms over time and evalu-ate the validity of this claim.

Ž .4 An examination of the relationship between afirm’s manufacturing flexibility, its placement on theproduct–process matrix, and organizational successshould be undertaken. One would expect successfulfirms to predominantly map to the diagonal of the

Žmatrix as predicted by prior researchers Hayes and.Wheelwright, 1984; Safizadeh et al., 1996 . The

possibility that off-diagonal firms are successful be-cause they have developed flexibility in their opera-tions and manufacturing function should also beinvestigated.

Ž .5 Several authors have proposed that differentŽstrategic approaches to flexibility exist e.g., De

.Meyer et al., 1989; Beckman, 1990 . The amountand types of uncertainty with which an organizationchooses to cope will determine its flexibility strategyŽ .Beckman, 1990 . For example, Japanese manufac-turers, in general, have attempted to eliminate uncer-tainty in supply and the production process. This hasallowed them to focus their efforts on reducinguncertainty in demand, which can also be addressedby other dimensions such as mix, new product, andvolume flexibility. The strategic emphasis on thesedimensions will vary across firms. The success ofdifferent flexibility building strategies should be em-pirically tested, and differences in outcomes due toorganizational structures identified at both the strate-gic and tactical level.

Ž .6 Several authors have noted the difficulty as-sociated with evaluating investments in flexible

Žtechnology e.g., Andreou, 1990; Ramasesh and.Jayakumar, 1991 . In addition, flexible technology is

generally more costly than non-flexible technologyŽ .Fine and Li, 1988; Gaimon and Singhal, 1992 . Ifthe flexibility provided by various investment op-tions could be more accurately assessed, it would beeasier to perform such a costrbenefit analysis. Theexplicit gains in flexibility provided by one technol-

Ž .ogy over another e.g., FMS and job shop could bequantitatively compared to the cost differential, andmarket implications thereby assessed.


Table 6Item measures for machine flexibility

Dimension Element Item Item Sourcenumber

Ž .Machine Range-number MF1 a typical machine can perform a large Barad 1992Ž .flexibility R-N percentage of the total number of operations

performed in the plantŽ .MF2 a large number of operations can be per- Benjaafar 1994

formed by more than one machineŽ .MF3 the number of different operations that a Sethi and Sethi 1990 ,

typical machine can perform is high Kochikar and NarendranŽ .1992 , Gupta and SomersŽ .1992; 1996 ,

Ž .MF4 a typical machine can use many different Sethi and Sethi 1990tools

Ž .Range-heterogeneity MF5 machines can perform many different types Carter 1986Ž .R-H of operations

Ž .MF6 the operations which machines perform are Gupta 1993Ž .very similar to one another R

Ž .MF7 existing machines cannot be used to per- Sethi and Sethi 1990Ž .form new operations modified

MF8 machines can perform various types of proposedoperations

MF9 machines can perform operations which proposeddiffer greatly from one another

MF10 machines can perform a variety of opera- proposedtions

Ž . Ž .Mobility M MF11 machine setups between operations are quick Browne et al. 1984 ,Ž .Chen et al. 1992 ,

Kochikar and NarendranŽ .1992

Ž .MF12 a lot of available capacity is used in Boyer and Leong 1996Ž .changing between machine operations modified

Ž .MF13 machine changeovers between operations Carter 1986are not expensive

Ž .MF14 machine tools can be changed or replaced Browne et al. 1984quickly

MF15 machine changeovers between operations proposedare easy

Ž .Uniformity U MF16 machines are equally efficient for all pro- Kochikar and NarendranŽ .cessing operations 1992

MF17 machines are equally effective, in terms of Mandelbaum and BrillŽ .quality, for all operations 1989

MF18 machines are equally effective, in terms of Mandelbaum and BrillŽ .productivity, for all operations 1989

Ž . ŽMF19 the processing time of an operation depends Benjaafar 1994 mod-.on the machine choice ified

Ž . Ž .MF20 the processing cost in dollars of an opera- Benjaafar 1994tion is not affected by machine choice

MF21 machines are equally reliable for all opera- Chandra and TombakŽ .tions 1992

MF22 all machines achieve similar performance proposedacross all operations


This is a limited sample of those studies thatwould be possible if valid, reliable, generalizablemeasures of flexibility were developed. Clearly, con-siderable work remains to be done before the con-cept of flexibility can be understood in its entirety.However, it is hoped that this study takes a step inthat direction, and is thereby helpful in facilitating amuch-needed movement towards the theoretical de-velopment of this area.

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