towards theory building in agile manufacturing strategy—a taxonomical approach

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Towards Theory Building in Agile ManufacturingStrategy—A Taxonomical Approach

Zhengwen (David) Zhang, Senior Member, IEEE, and Hossein Sharifi

Abstract—Agility has been widely accepted in manufacturingindustry as a new competitive concept. However, how to developa manufacturing strategy based around agility is not fully under-stood. This paper proposes a framework for the implementation ofagility as a manufacturing strategy and describes the developmentand analysis of a numerical taxonomy of agility strategies usingthe framework. The taxonomy was developed with cluster analysisbased on the relative importance attached to seven agility capabili-ties by a number of U.K. manufacturing companies. Three distinctclusters of strategy groups were observed across the industrystudied: Quick, Responsive and Proactive Players. Quick Playersare oriented towards a strong customer focus and quickness. Theydo not emphasize flexibility and responsiveness to changes andthey give low priority to proactiveness and partnership. Respon-sive Players are preoccupied with flexibility and responsivenessto changes. They do not emphasize proactiveness and partner-ships and they attach low importance to quickness. ProactivePlayers are characterized by high priorities on proactiveness andcustomer focus, high values attached to all capabilities, and highimportance given to partnerships. The underlying dimensions ofagile capabilities along which the three strategy groups differ wereinvestigated based on factor analysis and canonical discriminantanalysis. Changes/uncertainties in the business environment expe-rienced by members of different strategic groups were compared,and “manufacturing strategy choices,” in terms of manufacturingpractices for achieving agility, employed by members of differentstrategic groups were studied. Business characteristics and typicalcases for each strategy group were investigated.

Index Terms—Agile manufacturing, agility, manufacturingstrategy, technology management.

I. INTRODUCTION

I N the manufacturing strategy literature, two core elementsare central to the definition of a manufacturing strategy:

“Manufacturing Task” [1], [2] which is concerned with capa-bilities a manufacturing unit must have in order to competegiven the overall business and marketing strategy, and “Manu-facturing Choices” [2]–[5] which are concerned with decisionsmade by the manufacturing unit with regard to its facilities,technology, ways of integration, capacity, ways of organiza-tion, quality management, workforce policies, and informationsystems architectures [3]. The theory is that good alignmentof “Manufacturing Choices” with “Manufacturing Task” leads

Manuscript received October 1, 2005; revised March 1, 2006. Review of thismanuscript was arranged by Department Editor J. Liker.

Z. D. Zhang is with the Department of Engineering, School of Engineering,Computer Science and Mathematics, The University of Exeter, Exeter EX4 4QF,U.K. (e-mail: [email protected]).

H. Sharifi is with the School of Management, University of Liverpool, Liver-pool L69 3BX, U.K. (e-mail: [email protected]).

Digital Object Identifier 10.1109/TEM.2007.893989

to superior performance [1], [3], [6]–[15]. The work of Millerand Roth [13] is widely cited in the manufacturing strategyliterature [15]. Based on a taxonomical approach, the workidentified three types of manufacturing strategies, Marketeers,Caretakers and Innovators, commonly used by North Americamanufacturers at the time, together with manufacturing choicesassociated with these strategies [13], [15]. The strategieswere derived by clustering companies according to relativeimportance they place on 11 competitive capabilities includingcost, flexibility (design, volume), quality (conformance, per-formance), delivery (speed, dependability), after sale service,advertising, broad distribution, and broad product lines.

Significant changes have since taken place in the manufac-turing industry. A follow-up study using the same set of com-petitive capabilities and new data was conducted recently [15].The study found that while strategies for caretakers and innova-tors remained in existence the strategy for marketeers had beenreplaced by new forms of strategies [15]. An important aspectthat has not been considered in the studies is the emergence ofagility as a new manufacturing concept in the 1990s. Agilityrecognizes the significant impact of increasingly rapid changesfrom a dynamic business environment on manufacturing func-tions [16]–[18]. It argues for the inclusion of capabilities fordealing with rapid changes as a direct element of a manufac-turing strategy [19]. The term agility is often referred to as theability of enterprises to cope with unexpected changes, to sur-vive unprecedented threats from business environment, and totake advantage of changes as opportunities [17]. Following theargument, manufacturing task and choices need to be alignedto provide companies with the capabilities of coping with andexploiting changes as opportunities, and good fitness betweena manufacturing strategy and changes in the business environ-ment is expected to lead to good performance [18]–[20]. The last14 years have witnessed the widespread acceptance of agility asa new competitive concept. Different strategic capabilities re-lating to agility have been proposed and various manufacturingpractices and tools have been suggested as possible means forachieving agility [21]–[25]. Despite these, agility remains a con-cept rather than reality in industry [20], [25]. The problem isrooted in the fact that there is a lack of theory building withsound empirical evidence on this topic and most work has beenof a speculative and prescriptive nature rather than evidencebased [25]–[27]. The question of how to build agility in a man-ufacturing organization has not been answered satisfactorily.More specifically, what are the appropriate sets of capabilities(tasks) that need to be developed given different sets of changesfrom the business environment? What are the sets of manu-facturing practices and tools (choices) that need to be imple-

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mented for the tasks? How do the decisions relate to companycharacteristics?

The work presented in this paper attempts to make an initialeffort towards building answers for these questions. Followingthe manufacturing strategy literature [3], [10], [13]–[15], aframework for agility strategy development is formulated andvariables and constructs defining the framework identifiedthrough a literature review supported by a number of industrialcase studies. A numerical taxonomy [28] is built using theidentified variables. The taxonomy enables strategic groups ofmanufacturers which share similar “manufacturing tasks” interms of agility, i.e., emphasizing similar sets of agile capa-bilities, to be identified. Changes in the business environmentexperienced by members of different groups are then com-pared, and “manufacturing choices” employed by them studied.Though not providing direct answers to the questions identifiedabove, the work attempted to answer a subset of questionsforming the basis of developing answers for the listed ques-tions. The subset of questions includes: are there homogeneousgroups of companies sharing similar subsets of manufacturingtasks in terms of agility? Are the agility strategies of thesegroups shaped by changes in the business environment? Arethe groups of firms selecting manufacturing choices in linewith agility capabilities they intend to develop? To what extentdo manufacturing directors in different industries share similarviews on the strategic development of agility? To what extentthe fitness between changes in the business environment, man-ufacturing tasks, and choices can be observed for groups? Thepaper is organized into seven sections. Section II provides areview of the literature on agility. In Section III, a frameworkfor agility development is presented and variables/constructsfor the framework discussed in the context of academic liter-ature and supporting evidence from a number of case studies.Section IV describes the data/methods used in the analysis. InSection V, clustering analysis to identify the strategic groupsis described along with resulting groups. Factor and canonicaldiscriminant analysis are also carried out to identify under-lining dimensions of agile capabilities and factors separatingthe groups. In Section VI, we report on how the strategy groupsdiffer from one another in terms other than those used todefine them in the first place. Analysis of variance (ANOVA) isemployed to identify contextual variables, changes in the busi-ness environment, and manufacturing strategy choices whichcorrespond to the strategy groups. A summary of findings,conclusions and opportunities for further research are providedin Section VII.

II. AGILE MANUFACTURING

Several definitions of agility have been given. The Iacoccareport [16] stated that “agility is a comprehensive response tothe business challenges of profiting from rapidly changing,continually fragmenting, global markets for high quality, highperformance, customer configured goods and services” [17].DeVor et al. [29] defined agility as the ability of a producerof goods and services to respond quickly to rapidly changingmarkets driven by customer-based valuing of products/services.There are also specific definitions [30] such as “flexibility of

manufacturing, people and organization” [31] and “integrationof people, organization, and technology” [32]. Although def-initions may vary, a common thread focuses on being able tocompete and prosper within a state of dynamic change. Thisinvolves two aspects [19]: responding to changes (anticipated orunanticipated) in proper ways and due time; exploiting changesand taking advantage of changes as opportunities. In this paper,to fill the gap between agility and manufacturing strategy lit-erature, the definition given below is followed: “Agility is amanufacturing strategy that aims to provide manufacturing en-terprises with competitive capabilities to prosper from dynamicand continuous changes in the business environment, reactivelyor proactively.” First, agility is considered as a manufacturingstrategy, which will consist of manufacturing tasks and choices.Second, the tasks and choices should seek to provide the com-petitive capabilities to enable the enterprise to prosper fromdynamic changes in the business environment. Third, prosperingfrom changes reactively and proactively requires both the abilityof responding to changes in proper ways and due time, and theability of creating changes and exploiting changes as opportuni-ties. In fact, uncertainty or change in the business environmenthas been a major topic for management research, long before theterm agility was introduced. Thompson [33] suggested that oneof the most important tasks for any organization was to manageuncertainties. Drucker [34] described the concept of entrepre-neurial task as the search for changes, response to changes, andexploitation of changes as opportunities. However, today’s rateof change is increasing exponentially. Turbulence and uncer-tainty in the business environment have become the main causeof failures in the manufacturing industry [35]–[37], necessitatingthe consideration of agility as a manufacturing strategy.

Work presented in the literature generally falls into threecategories. The first consists of work that proposes manufac-turing practices/tools as enablers for agility, thus is concernedwith “manufacturing choices” for agility. Work included prac-tices, methods and tools to support product and manufacturingsystems design [30], [38]–[40], dynamic process planning[41]–[44], responsive production scheduling [45]–[48], facilitylayout [49]–[51], materials handling and storage [52], [53],information systems and virtual enterprises [25], [54]–[59],supply chain integration [60]–[63], and the creation of empow-ered and flexible workforces and organizations [64]–[67].

The second is concerned with conceptual models and frame-works for achieving agility. This includes early work describingwhat agility is, what competitive capabilities are relevant, andwhat characterises an agile enterprise, as well as later workproposing methods to support the implementation of agility. TheIacocca report [16] presented a comprehensive overview of theconcept of agility, the background, as well as a list of charac-teristics an agile enterprise should possess. Goldman et al. [17]suggested four agility dimensions: enriching customers, cooper-ating to enhance competitiveness, organizing to master change/uncertainty, and leveraging the impact of people and informa-tion. Preiss et al. [68] suggested a preliminary method to mea-sure changes in the business environment and to help a companymove towards agility. The method was implemented througha series of worksheets that guide the company to formulate aprioritized list of issues to be dealt with. Other works in this

ZHANG AND SHARIFI: TOWARDS THEORY BUILDING IN AGILE MANUFACTURING STRATEGY—A TAXONOMICAL APPROACH 353

area have tried to derive conceptual and practical models for im-plementing agility. Kidd [32] suggested that agility is achievedthrough the integration of organization, people, and technologyinto a coordinated, interdependent system. Dove [69] proposeda more detailed reference model in which a list of changes in thebusiness environment was identified and agility was developedthrough the improvement of a business’s responsiveness to thesechanges. Booth [70] proposed a path to agility involving orga-nizing around processes, forming concurrent teams and workcells, and improving the use of information systems. Youssef[71] suggests three pillars of achieving agility: customers, in-ternal capabilities, and suppliers. In the work of Gehani [72],business abilities such as quickly satisfying customized orders,introducing new products frequently in a timely manner, andgetting in and out of strategic alliances speedily were specifiedas the main elements. More recently, Gunasekaran [24] put to-gether a framework for developing agile manufacturing systemsalong four dimensions: strategies, systems, technologies andpeople. Based on a number of industrial case studies, Bessantet al. [73] proposed a reference model that seeks to guide thedevelopment of agility within enterprises. Other contributionsinclude the work of Coronado et al. [58] which attempted toidentify information systems requirements for agility, the workof Ren et al. [74] which proposed to implement agility throughthe deployment of agile attributes, and the work of Zhang andSharifi [19], which proposed to implement agility through theidentification of drivers and the deployment of capabilities tocope with drivers.

While these models and frameworks help define details ofagility as a new concept, they are of a prescriptive and concep-tual nature. Theory building with empirical evidences is rare.However, there has been some recent work in this direction,which forms the third category of work in the literature. Yusufand Adeleye [27] attempted to develop hypotheses about agilitywhich assume linkages between a list of agility drivers and ca-pabilities. The hypotheses were tested through a questionnaire.Cao and Dowlatshahi [25] examined the impact of alignment ofinformation technology and virtual organization on the perfor-mance of agile manufacturers.

Another part of literature worth noting is concerned withmanufacturing flexibility. Though some authors [31], [32],[75] equated agility with flexibility, later works reportedclear differences between the concepts. In particular, Chester[76] maintained that systems such as Flexible ManufacturingSystems were facilitators of successful management of manu-facturing by concentrating on the mechanics of factory floorswithout taking the sight of customers and competitors. Baker[77] proposed a typology in which agility was considered at ahigher level over flexibility where strategic views and networkof relations were considered. In later work [78]–[81], agilityand flexibility are distinguished by relating the latter to the com-pany’s production system and former to the company-marketinterface.

III. FRAMEWORK AND CONSTRUCTS

The proposed framework that defines agility as a manufac-turing strategy consists of four main elements, as shown in

Fig. 1. The proposed framework of agility as a manufacturing strategy.

Fig. 1. The first represents changes and pressures from the busi-ness environment which acts as driving forces for consideringagility as a manufacturing strategy. These are referred to as“agility drivers.” Agility drivers necessitate a company to seeknew ways of running a manufacturing unit in order to maintainits competitive advantage. The second is a statement of “manu-facturing task,” given pressures from the business environment.For a company, “manufacturing task” would comprise a list ofprioritized capabilities that the company intends to focus on inorder to positively respond to and take advantage of changes.The task, as revealed by ratings of capabilities, would indicatethe “strategic intent” of the company [13], [37]. The thirdis a generic list of competitive capabilities for agility, fromwhich manufacturing task is formulated. These are referred toas “agility capabilities.” The fourth is about “manufacturingchoices” for agility. Like traditional manufacturing strategies,these are concerned with decisions made by the company withregard to its facilities, technology, ways of integration, capacity,organization, quality management, workforce policies, andinformation systems architectures. They are referred to as“agility providers.”

Based on the framework, a manufacturing enterprise experi-ences various changes in its business environment which drivethe enterprise to prioritise “agility capabilities” that need to bedeveloped in order to cope with and take advantage of changes.This in turn forces the enterprise to search for methods to ob-tain the required capabilities. Performances from implementa-tion can be evaluated and results provided as feedback to adjustthe strategy. From the literature on agility and manufacturingstrategy, the four elements are all essential in defining a frame-work for agility as a manufacturing strategy. Drivers are the es-sential driving forces for agility as a manufacturing strategy.Task, capabilities and choices are the essential elements of amanufacturing strategy. Constructs defining the framework in-clude different types of changes from the business environment(“Agility Drivers”), different agility capabilities, and variousagility providers.

A. Agility Drivers

In the literature, various changes have been cited as drivingforces for agility. The frequently cited include the increasedcompetition from globalization, fragmenting markets, short-ening product life cycles, dynamic changes in demands, faster


rate of product introduction, faster pace of innovation, individu-alizing customer requirements, the advancement of informationtechnology (IT), and the increasing pressure from environ-mental legislations [16]–[18], [26], [27], [32], [68]–[71], [82].They may be broadly grouped under five dimensions of busi-ness environment: marketplace, competition criterion, customerrequirements, technology, and social factors [19].

As suggested by the literature, there has been a significantrecent change of the marketplace in which we operate, due toglobalization. Examples include the growth of niche markets[16], the rapid change of product models in the marketplace,and the shrinkage of product lifetime [17]. In addition, changesare taking place on ways in which companies compete (competi-tion criterion), owing to fierce competition amongst enterprisesin cost, quality, technology, time, responsiveness, etc [19]. Pres-sures from customers, empowered by new information and com-munication technology (ICT), are evident, in terms of demandfor individualized products and services, quicker delivery timeand time to market, higher quality, and after sale services [20],[27]. Threats to businesses also come from the rapid pace oftechnology development, the availability and wide accessibilityof new product technology, new manufacturing processes, andnew ICT technology which impacts the way businesses operate.Faster pace of technology change is often cited as an importantdriver for agility [29], [32]. Changes in “social factors,” in termsof pressures from environmental issues, the workforce, legal/po-litical systems, culture issues, and the way in which social con-tract is organised, are also becoming important [22], [23].

B. Agility Capabilities

In manufacturing strategy, competitive capabilities are oftenexpressed in broad terms such as low cost, flexibility, quality anddelivery [1], [3], [7], [83], [84]. Recently, flexibility has beensubdivided to include design and volume flexibility, quality tocomprise conformance and performance quality, and deliveryto incorporate speed and dependability [13]. The list has alsobeen extended to incorporate innovativeness, time, after saleservice, advertising, broad distribution, and broad product lines[13], [85]. In agility, speed is considered an essential elementof agile capabilities. The literature [16] suggested that agilecompetitors are first fast operators. However the meaning of“speed” has been extended. In the manufacturing strategy lit-erature [13], speed is often referred to as the “capability to de-liver products quickly.” In agility [23], [24], speed is referred toas the “capability to operate quickly in all aspects of productdevelopment/manufacturing.” Flexibility is another capabilitythat is frequently cited in the literature. Most work recognises itas one of the most important capabilities for agility [77]–[81].The meaning of flexibility has also been broadened to incor-porate not only design and volume, but also people, resources,and organization flexibility to enable an enterprise to respondto changes. The most frequently cited capability in the litera-ture is responsiveness to changes. This refers to the capabilityto identify, respond to and recover from changes. Most agilitydefinitions incorporate the terms of “responding to changes” and“exploiting changes as opportunities” [16], [17], [30]–[32]. The

literature also suggests that an agile competitor must be a com-petent player [32], [68]–[70], i.e., it must have the basic busi-ness competency in terms of cost, efficiency, quality, as wellas product and delivery performance. It must be able to inno-vate, and develop and manage core competency. Other capabil-ities suggested include “focusing on customer,” “partnership,”and “proactiveness.” Goldman [17] proposed that “enrichingcustomer” and “cooperating to enhance competitiveness” wereimportant dimensions of agility. Similar capabilities were sug-gested by Preiss et al. [68]. Proactiveness, the capability to actproactively to take advantage of changes, is another angle thatis incorporated in most definitions of agility [16], [17], [24],[30]–[32]. This study used seven competitive capabilities de-fined as follows.

• Proactiveness—The capability to act proactively instead ofreactively (in attacking threats and opportunities).

• Responsiveness—The capability to identify, respond toand recover from changes.

• Competency—The capability to operate efficiently, pro-duce high-quality and high-performance products, deliveron time, innovate, and manage core competency.

• Flexibility—The capability to perform different tasks andachieve different objectives with the same set of resources/facilities.

• Quickness—The capability to operate at high speed.• Focusing on customer—The capability to have a strong

customer focus.• Partnership—The capability to form concrete relationship

with suppliers and to partner.Others [69], [86] have suggested similar lists. Dove [69] con-

sidered modularity, plug compatibility, dynamic relationships,reusability of facilities, and open system framework as capabil-ities for agile manufacturing. Reid et al. [86] suggested a listof agility attributes including sensing and anticipating changes;adaptability; ability to recover from change; quickness; innova-tion; flexibility; and efficiency.

C. Agility Providers

In the literature, various manufacturing practices and toolshave been proposed as means for achieving agility [38]–[67].They are concerned with technology, people, or organizationaldecisions in a manufacturing business. Kidd [32] suggestedthat agility may be achieved through the integration of people,organization and technology into a coordinated system.Gunasekaran [24] considered agility practices and tools fromfour categories: technology, people, systems and strategies.Sanchez and Nagi [66] considered practices related to productand manufacturing systems design, production planning andcontrol, information systems, supply chains, human factors andbusiness processes. In constructing the list of agility practicesas manufacturing choices, we considered practices genericto different businesses and expressed them in broad terms sothat each may be implemented with different techniques indifferent industries or types of business. This was to ensurethat generic practices were used as manufacturing choices,rather than techniques specific to a certain type of operationor circumstance. Practices from the literature were synthesizedfrom the following areas: relationships with suppliers and


competitors, technology, people, organization, integration,innovation, relationships with customers, and informationsystems. Apart from practices concerning technology, people,and organization, the ways in which these are integrated areconsidered [32]. In addition, issues concerning innovationwere included in recognition of the importance of continuousinnovation in agility [16]. As supply chain integration andvirtual organization were often considered important meansfor achieving agility [24], [66], we’ve incorporated practicesconcerning the relationship with suppliers/competitors, as wellas those concerning the relationship with customers. Anotherset of practices considered are those related to informationsystems (IS). IS tools and practices are vital in integratingother practices together and have been demonstrated to have apositive impact on agility performances [25].

Exploratory case studies involving 12 companies from anumber of sectors were carried out to support constructs de-velopment. The studies, based on structured interviews withsenior directors, were designed to clarify the details of drivers,capabilities and practices. The cases were selected from 30companies who had indicated an interest in taking part in suchstudies from an early pilot study. The interviews comprisedfive parts: background, agility drivers, agility capabilities,agility practices, and agility circumstances. During the studies,a list of drivers, capabilities and providers compiled from theliterature was provided and companies were asked to indicatewhether each item was relevant to them and in what way itwas relevant (how each driver had affected them, how eachcapability helps to address the drivers, and whether and howthey had implemented each provider). The companies werealso asked to provide a specific list of drivers they were facedwith along with specific capabilities and practices they hadconsidered. Items from the original list which were unimportantto most firms were replaced by those not on the list but sharedby several companies. The final list is given in Table I.

IV. RESEARCH METHODS

A. Sample

Data was obtained from a survey of 900 U.K. firms, selectedfrom the Department of Trade and Industry database, a compre-hensive source of U.K. businesses. The survey was originallydesigned to target three main sectors, auto parts, aerospace,and electronics. The reason was that there had been severalindustry-wide initiatives promoting the concept of agility inthese sectors, and it was assumed that firms from these sectorsmight be more likely familiar with the concept or practices foragility. In the sample, firms from these sectors each amountto 25%. The remaining 25% were selected from other sectorsto check for sector differences (they were from machinery,white goods, food/drink, rubber/plastics, and textiles). Foreach 25%, firms were randomly selected (micro-businesseswith few people were excluded on the assumption that theywould unlikely be able to make a contribution to such a newconcept). The final sample comprised firms ranging from smallbusinesses of less than 50 people to large businesses with over2000 employees. Annual turnover ranged from under m toover m. The sample thus represents an important portion

of manufacturing industry and covered a wide range of firms.As will be reported later, sector differences in agility strategieswere tested for and no statistically significant evidence wasfound. The literature also gives no evidence for sector differ-ences. Of the 900 firms, 200 randomly selected were initiallysurveyed as pilots and responses used to inform instrumentrefinement. The final instrument was sent to the remaining700 firms, accompanied by an explanatory text to describe theconcept, the purpose of survey and the definitions of terms,including various drivers, capabilities and practices. A total of79 responses were received from the two stages, resulting ina response rate of 8.8%. The response rate was not high butgiven that agility as a manufacturing strategy was new and thelarge number of questions involved, the number of returns wasconsidered satisfactory. Nonresponse bias was examined usingChi-square, to establish how well the respondents representthe sample. No significant differences were found for sectordistributions , number ofemployees , and turnover

. Table II highlights thedescriptive statistics of respondents and the sample. Of 79responses, 74 were valid. Those excluded were substantiallyincomplete questionnaires. 16 of the 74 valid responses werefrom the pilot stage and used to inform instrument development,the remaining 58 used in full analysis.

B. Respondents

The questionnaire was addressed to a single informant, typi-cally Managing/Engineering/Operations Director. There was asection in the questionnaire for the actual respondent to pro-vide his/her name and position in the firm, and to sign and datethe form. The names/positions appearing on the returns do notall agree with the original names/positions to whom the ques-tionnaire was addressed, but the returns, as indicated by theforms, appear to be completed by high ranking informants. Outof the 58 returns used in the final analysis, 15 (25.9%) respon-dents held the titles of Manufacturing/Operations Directors, 10(17.2%) Engineering Directors, 12 (20.7%) Managing Direc-tors, 19 (32.8%) Manufacturing/Technical/General Managers,and 2 (3.4%) others (Quality Director/Project Manager). Closestudies also found that in the case of Manufacturing/Technical/General Managers, 9 (almost 50%) were from subsidiaries oflarge international businesses. As suggested by literature [13],the problem of questionnaire surveys involving single informantis that strong assessment of convergent or discriminate validitycan not be made. However, the cost associated with gainingparticipation and consensus from several individuals from largenumbers of organizations is very high. The use of high rankinginformants tends to offset this problem [87]. We have checkedwhether the questionnaires were actually completed by the re-spondents recorded on the forms. In the questionnaire, we askedthe respondents to indicate whether they would like to take partin further in-depth case studies. Twenty companies indicated aninterest. Following the questionnaire returns, we had telephoneconversation with respondents to schedule on-site studies, andarranged case studies with 14 companies within our timescale.The conversations confirmed that the respondents had a goodknowledge of the questionnaire, a strong indication that they


TABLE ICONSTRUCTS AND INTERNAL RELIABILITY (CRONBACH’S COEFFICIENT ALPHA)

actually completed the forms. The 14 detailed case studies con-ducted later, where at least one other individual was involved in

addition to the original respondent, also confirmed that data inthe original returns are reliable.


TABLE IIDESCRIPTIVE STATISTICS OF THE SAMPLE AND RESPONDING COMPANIES

Reliabilities were checked for each major construct, basedon Cronbach’s alpha. For drivers, alpha values were calculatedfor each category and for the whole set. The values were allwell above 0.7 recommended [25]. For capabilities, Cronbach’salpha was 0.704. For providers, alpha values were calculatedfor each subset of providers and for the set as a whole. Theset as a whole gave an alpha value of 0.938. Most subsets hadalpha values above 0.7, except “Relationship with Suppliers/Competitors” (0.599), “Technology” (0.542), and “Innovation”(0.533). While the three subsets had alpha below 0.7, we wouldargue that they were acceptable given that the total set had a veryhigh alpha (0.935) and values for the three subsets were near orabove 0.55. The literature suggests [25] that for concept whichis new a lower alpha is acceptable. Care was also taken duringinstrument development to maximize the validity of constructsand the clarity of questions, which will be reported next.

C. The Instruments

The questionnaire consisted of five broad categories ofquestions. The first determined the profile of the company. Thisset of questions aimed to obtain basic data about the companiesand their products/operations, including sectors, sizes, numbersof products, types of production systems, percentages of exportsale, market positions, numbers of suppliers/customers/part-ners, percentages of sales invested in R&D, rates of new productintroduction, success rates of new products, and awareness andperceived needs for agility. The second category addressedchanges in the business environment. Each company was askedto rate to what extent each listed change has influenced his/hercompany in recent years, on a five-point Likert scale, where“1=not important” and “5=highly influential (vital).” The thirdcategory addressed agility capabilities the respondents plannedto pursue given the pressures from changes in the businessenvironment, and thus the manufacturing task. Executives wereasked to rate each capability separately on a five-point Likertscale. The ratings indicated the relative importance attributedto each capability that the company chose to emphasize, where“1=not important” and “5=critically important.” The fourth partprobed for key action programs the respondents intended topursue in the next two years given changes in their business en-vironment. The respondents were asked to rate, on a five-pointLikert scale, the importance they attached to each “agilityprovider,” where “1=not applicable” and “5=highly important.”This set of questions aimed to establish the pattern of choicesin the manufacturing strategy. We have also probed the degreeof implementation where a provider has been implemented,and the perceived effectiveness. The fifth section looked at the

perceived barriers to achieving agility. These resulted in over120 questions and 223 separate data elements. The industrialpooling problem [88] was avoided by wording the questions sothat respondents rated their changes, capabilities, and actionsrelative to their particular competitive situations, rather thanusing industry in general as a reference. The instrument wasdeveloped through an iterative process. First, constructs forthe framework were grounded on the academic literature.These were refined through exploratory case studies involving12 companies, as discussed in Section III, to make sure theyreflect practical concerns in industry. The resulting instrumentwas discussed and pretested with firms taking part in the ex-ploratory case studies, during which the internal reliabilitieswere checked based on Cronbach’s alpha and items with lowscores redefined. The instrument was then sent to 200 firms fora pilot-survey. Based on reliabilities, further refinements weremade to produce the final instrument. The reliability of returnsfrom the full survey was checked again based on Cronbach’salpha, as reported in Section IV-B.

D. Methods for Clustering

In [13], a nonhierarchical algorithm (k-means) was used.This study employed a two-stage approach where a hierarchicalmethod was used to help determine the number of clustersand cluster centroids which were then input into a subsequentk-means algorithm [28], [15]. Ward’s partitioning and squaredEuclidean distance were used in the hierarchical stage tomaximize within-cluster homogeneity and between-clusterheterogeneity, and to recover known cluster structure [89], [90].

V. CLUSTER, FACTOR AND CANONICAL

DISCRIMINANT ANALYSIS

A. Cluster Analysis

Cluster analysis was used to identify agility strategy typesfrom respondents’ capabilities profiles. Of the 58 cases, onewas dropped due to missing data. The rest all contributed tothe final clusters. A problem with cluster analysis is how todetermine the most appropriate number of clusters. This paperemployed a combination of methods used by other researchers[13], [15]. First, hierarchical clustering was carried out to helpdetermine the number of clusters. The dendogram for clusteranalysis was visually inspected for relatively dense branches toconfirm the number of major groups formed [89] and the incre-mental changes in the agglomeration coefficient during clustercombination stages were observed as an indication of whetherdissimilar clusters have been merged [15], [28]. A three cluster


TABLE IIIAGILITY CAPABILITIES BY GROUP

model appeared to be the most appropriate. K-means was thenused to perform actual clustering. We used ANOVA and pair-wise comparison tests of mean differences [92] to check cross-cluster heterogeneity of the resulting solution on the definingvariables. The resulting clusters significantly differed in 6 outof 7 capabilities . An overall multivariate test ofsignificance using Wilk’s Lambda and the associated F-statis-tics indicated that the null hypotheses that the three clusterswere equal across defining variables could be easily rejected

. Second, solutions were tested with the numberof clusters varied from 2 upwards. The tightness of resultingclusters measured by Pseudo-F-statistics and managerial inter-pretability were checked. The three cluster solution was foundto be the most appropriate. A two cluster solution would re-sult in one group with high values on every capability and onelow on every one. There was also a profound increase in clustertightness from two-cluster to three-cluster solutions. However,when the cluster number was increased to four, one of the clus-ters in the three-cluster solution was broken up into two similarsubgroups. There were differences between the subgroups, butfor interpretation purposes, there was no strong argument thatthey were different. While the Pseudo-F statistics had a slight

increase on three variables, they were decreased on other vari-ables. We then moved from 4 to 5 clusters. One of the othergroups in the three-cluster solution was broken up, again re-sulting in two similar subgroups which were not distinguishablefrom the point of view of other groups. Also the pseudo-F statis-tics became smaller for most variables. A cross-validation [13]was carried out to check for the reliability and stability of the so-lution. The three resultant agility strategic groups are describedin Table III in terms of their group centroid (mean) scores andrelative ranking for the 7 capability variables. The probabilitythat one or more of the cluster means differ from another is alsodepicted for each capability. The clusters differed on six vari-ables at the 0.05 level of significance or less. The interpretationof the groups is given below. It is predicated upon: 1) whetherthere are significant differences on the cluster means of the ca-pability variables at 0.05 level or less; 2) relative ranking of theimportance of a capability within a cluster.

1) Cluster 1—Responsive Players: We label cluster 1 “Re-sponsive Players” because of its emphasis on responsiveness tochanges. Responsiveness to changes is ranked first, followed byflexibility and competency, then proactiveness, customer focus,partnership and quickness. We conducted a pair-wise test of


TABLE IVRESULTS OF PRINCIPLE COMPONENT ANALYSIS

mean differences between capabilities to determine the signifi-cance of the relative ranking. Responsiveness, flexibility, com-petency, proactiveness and customer focus were ranked in a topset and there was no significant difference between them (e.g.,responsiveness versus customer focus, ).Likewise, quickness and partnership were ranked in a lowerset and there was no significant difference between them

. However, differences between capabilitiesfrom different sets were significant (e.g., responsiveness versusquickness, ; responsiveness versus part-nership, ; customer focus versus quick-ness, ; customer focus versus partnership,

). Although no statistical differences werefound on the ranking of the five capabilities in the top set, acrossclusters the cluster gives significantly lower score to customerfocus. Likewise, the score given to proactiveness is significantlylower than that given by the proactive cluster, and is not statis-tically different from that given by the quick cluster. For com-petency, the scores given by the three clusters were not signif-icantly different. Comparatively, the scores for responsivenessand flexibility are significantly higher than those given by thequick cluster and are statistically similar to those given by theproactive cluster. It appears that members intend to compete bydeveloping their ability to respond to changes with flexibility.They do not put high emphases on proactiveness and partner-ship, and they give the lowest scores for quickness and customerfocus compared to other clusters. Flexibility and responsivenessare associated, as a firm would not be able to respond to changesif it is not flexible.

2) Cluster 2—Quick Players: This cluster ranks customerfocus first, then quickness, competency, flexibility, responsive-ness, proactiveness and partnership. A pair-wise test of meandifferences found three sets of capabilities in relative ranking.The top set consisted of customer focus. The middle set includedquickness, competency, flexibility, responsiveness, and proac-tiveness and the bottom set partnership. There were significantdifferences between capabilities from different sets (customerfocus versus quickness, ; proactivenessversus partnership, ), but capabilitieswithin the middle set did not differ significantly (e.g., quicknessversus proactiveness, ). Across clusters,the cluster gives significantly lower scores to flexibility and re-sponsiveness. The score for proactiveness is also the lowest. Incontrast, the score for quickness is significantly higher than that

given by responsive cluster and is similar to that given by proac-tive cluster. The emphases of this cluster seem to be on quick-ness, in addition to customer focus. It appears that membersplan to compete by focusing on customers and speed of oper-ations. They do not put emphases on flexibility and responsive-ness and they give the lowest score and ranking to proactivenessand partnership.

3) Cluster 3—Proactive Player: This cluster ranks customerfocus and proactiveness first, followed by responsiveness,competency, flexibility, partnership and quickness. A test ofmean differences found two sets of capabilities in relativeranking, with customer focus and proactiveness in the topset and the rest bottom (customer focus versus proactiveness,

; proactiveness versus responsiveness,; responsiveness versus partnership,

). Although relative ranking for flex-ibility, responsiveness, quickness and partnerships are low,across clusters these are still the highest scores given. Membersappear to compete on all capabilities. Compared to others, theyput significantly higher scores on proactiveness and partner-ship. A logical explanation is that in order to be proactive, theyhave to be quick, flexible and responsive. It is also important topartner with suppliers/customers.

B. Underlying Dimensions

The 7 taxons representing the importance of agility capabili-ties succeeded in generating a numerical taxonomy to describeagility strategies. However, the 7 variables are correlated witheach other. Therefore we used multivariate statistical methodsto extend exploration. First, factor analysis based on principalcomponent method [93] was carried out to identify under-lying factors that explain the patterns of correlation withinagility capability variables, thus bringing out the underlyingdimensions which had contributed to the formation of the threegroups. Three principal components (factors) were found withEigen values greater than one. Together they explain 66.65%of variances in the data. Table IV shows the rotated factormatrix, which summarises variances of the original variablesrepresented by the factors. Factor 1 represents “proactivenessand partnership,” factor 2 “quickness and customer focus,” andfactor 3 “flexibility and responsiveness.” Clearly, “proactiveplayers” have chosen to compete along the dimension of factor1, “quick players” along factor 2, and “responsive players”along factor 3.


TABLE VRESULTS OF CANONICAL DISCRIMINANT ANALYSIS

TABLE VICANONICAL LOADINGS AND COEFFICIENTS

While factor analysis helps us to understand underlying datastructures, a multi-group discriminant analysis will help us toidentify underlying dimensions that separate the groups fromeach other. Similar to [13], a canonical discriminant analysiswas carried out, with each of the taxonomic groups as criterionvariables coded into dummies and with the 7 agilitytaxons comprising the predictor set [94]–[96]. Standardizedestimates for both canonical structure loadings and canonicalcoefficients were obtained. The canonical loadings representthe correlations of the original variables in the predictor setwith an underlying unobserved dimension (each dummy dis-criminant function). So the loadings are useful as indicators ofwhich original variables are most correlated with each canon-ical function [96]. The standardized canonical coefficients areanalogous to beta weights in regression and can be used topredict cluster membership. Based on these we’ve also usedmultiple group discriminant classification to cross-validate thecluster solution resulting from the clustering stage. Table Vcontains the results of the canonical discriminant analysis usedto investigate the relationship between the 7 taxons and clustermembership. Two canonical functions resulted. Function 1,with an Eigen value of 2.189, represented 65.3% of totalvariance in the data and had a canonical correlation of 0.829,which measures the relative strength of relationship betweenpredictor variables and the group membership. Function 2,with an Eigen value of 1.162, represented the remaining 34.7%of total variance and had a canonical correlation of 0.733.Both correlations were significant. A test of multivariate rela-tionships between the functions and the predictor set gave aWilk’s Lambda of 0.145 and 0.463 and significance levels of0.000, indicating that significant overall relationships betweenthe discriminant functions and predictor variables do exist.Table VI lists structure loadings and standardized canonicalcoefficients. According to the loadings, function 1 has largerabsolute correlation with proactiveness, partnership, flexibility,

responsiveness and competency, while function 2 with quick-ness and customer focus.

The five variables that function 1 has a stronger correlationwith are concerned with capabilities for dealing with changes,either reactively or proactively. The correlations are positive,implying that function 1 may represent the “change proficiency”dimension, as conceived by Dove [18] that depicts the require-ment of firms to deal with changes reactively or proactively.The large canonical coefficients with proactiveness and partner-ship ( [13]) suggest that firms placing high emphases onthe two capabilities will fall at the high end of the dimension.The smaller but positive canonical coefficients with flexibilityand responsiveness suggest that firms putting high emphasison these capabilities may appear somewhere in the middle ofthe “change proficiency” dimension. Those placing low em-phasis on all will likely appear at the lower end. This corre-sponds well with Dove’s change proficiency model suggestingthat firms could move from being irresponsive (low proficiency),to being responsive to changes (medium proficiency), then tobeing proactive (high proficiency). Canonical function 2 has astrong positive correlation with quickness and customer focusand a weaker negative correlation with responsiveness and flexi-bility. The large positive coefficients for quickness and customerfocus suggest that firms placing high emphases on these capabil-ities will be assigned to the high end of the spectrum. The neg-ative coefficients for flexibility and responsiveness to changesimply that firms emphasizing these two will be assigned to thelower end. This function may be labelled as the “speed to cus-tomers” dimension which emphasizes the capability of firms toget to customers quickly. Interestingly, the “speed to customers”dimension corresponds well to Goranson’s agility type 1 (satisfyand be close to customer) [97], while the “change proficiency”dimension to Goranson’s agility type 2 (respond to changes an-ticipated) and 3 (respond to changes unanticipated). Fig. 2 plotsfirms on a space formed from the two dimensions, where dimen-


TABLE VIINUMBER OF OBSERVATIONS AND PERCENT CROSS VALIDATED

TABLE VIIIINDUSTRY REPRESENTATION BY STRATEGY GROUP (RESULTS OF SECTOR * CLUSTER NUMBER CROSS TABULATION)

Fig. 2. Plot of respondent business units and group centroids on canonicalfunctions.

sion 1 and 2 represent horizontal and vertical axes respectively.It shows that proactive players occupy the upper right corner ofthe space, suggesting that they compete along both “change pro-ficiency” and “speed to customers” dimensions. Quick playersoccupy the upper left corner of the space which means that theyprimarily compete along the “speed to customers” dimension.Responsive players occupy the mid-right bottom of the space,suggesting that they compete along the “change proficiency” di-mension. It is also noticeable that the bottom right half corner

is unoccupied. This is the place for firms who focus on beingvery “proactive” but with low “speed,” a less-likely combina-tion. The bottom-left corner, representing “not change profi-cient” and “low speed,” is unoccupied, probably implying thatfirms with such a strategy would not survive for long.

C. Statistical Cross-Validation

To determine the stability of estimates of agility clusters, across-validation test was carried out. The clustering solutionwas used to build discriminant functions to be used for the clas-sification of future observations. The probability of misclassi-fication was estimated through cross-validation. Using a jack-knife procedure, a discriminant function with the 7 taxons aspredictors was computed for 56 out of 57 cases, and the functionwas used to classify the one observation held out. The processwas repeated for each of the 57 cases and the proportion ofhold-out observations assigned to each group was determined.The error rates reflect the percentage of hold-out cases misclas-sified. As shown in Table VII, 77.8% of cluster 1, 85.7% ofcluster 2, and 88.2% of cluster 3 are correctly classified.

VI. ANALYSIS AND DISCUSSIONS

A. Industrial MIX and Contextual Variables

The relationships between agility cluster and industrysector memberships were studied based on cross-tabulation(Table VIII). A chi-square test indicates no significant associa-tion between cluster number and industry sector .All sectors had members competing with each of the agility


TABLE IXCONTEXT VARIABLES BY STRATEGIC GROUP (ITEMS WITH SIGNIFICANCE VALUE SMALLER THAN 0.09)

strategies, although aerospace sector appeared to have a lowerpercentage of quick players, auto parts had relatively lessresponsive players, and electronics had less proactive players.The differences between sectors were not significant statisti-cally to suggest a sector bias.

We also checked differences between the three groups alongseveral contextual variables using ANOVA and Scheffe pairwise comparison tests of mean differences [92], as in Table IX.Responsive players were found to have a higher percentage ofcomplete innovation in new product development. They alsohad a significantly lower new product success rate comparedto other groups, which may be due to the risk associated withcomplete innovation. Quick players had significantly shorterconcept to cash lead-times for new products, which is consistentwith their manufacturing tasks. Although statistically insignif-icant, proactive players seemed to have higher percentage ofsuppliers taken as partners and introduce more new products.Other contextual variables tested include percentage of exportsale and percentage of turnover invested in R&D. No statis-tically significant differences were found. We suspected thatquick players might try to achieve speed to customers throughproduct tailoring/configuration (thus lower proportion of com-plete innovation, higher new product success rate, and shorterlead-time from concept to cash) than responsive players, whileproactive players might fall between (as they operate alongboth speed to customer and changes proficiency dimensions).The difference might lay on the nature of markets and thestages of product lifecycles. Unfortunately, the instrument didnot include questions about these and future work should aimto find this out.

B. Business Environment

Respondents were given 27 changes in the business en-vironment and each asked to rate to what extent each hadinfluenced his/her company in recent years, on a five-pointLikert scale, where “ important” and “influential.” Responses from the three clusters were comparedto find if and how their strategies were shaped by pressuresfrom the business environment. The clusters differed in 16

agility drivers as shown in Table X. “Quick players” gavethe lowest mean values on all 16 drivers. Responsive playerssuffered from higher pressures than quick players on “productlifetime shrinkage,” “rapidly changing market,” “innovationrate increasing,” “decreasing new product time to market,”“individualizing products/services,” and “quality expectationincrease.” High pressures from changes in market and demandsfor innovation and new/individualized products perhaps pushedresponsive players to be more flexible and responsive, whilelow pressures gave quick players little motivation to do so.Quick players, with the lowest pressures on all 16 drivers,had chosen to compete by increasing market share throughcustomer focus and high speed of operation. Proactive playerssuffered from the highest pressures on all drivers. Their re-sponses to all drivers were significantly higher than those ofother groups, which may explain why they intend to competealong all dimensions. In addition to even higher pressures frommarket changes and demands for individualized products, theyalso suffered from “rapid changes in product models,” “effec-tiveness of competitors’ strategy,” “quicker delivery time andtime to market,” “change of technology,” and “social factors.”Being proactive and forming partnership was often cited in theliterature as a way to improve time to market, speed of newproduct introduction (NPI), and technology take-up.

C. Action Programs

In the survey, respondents were given a list of 48 key ac-tion programs (agility providers) to improve the effectivenessof their operations in light of changes they face. The respon-dents rated, on a five-point Likert scale, the importance theyattach to each “agility provider,” where “ applicable”and “ important.” The action programs indicatethe intended emphasis on manufacturing choices and tap im-portant underlying structural and infrastructural directions ina manufacturing strategy [13]. Responses from the three clus-ters were compared to find if and how well their manufacturingchoices were aligned with tasks in their agility strategies. Weused ANOVA and Scheffe pairwise comparison of mean dif-ferences to carry out the study. The manufacturing choices of


TABLE XAGILITY DRIVERS BY STRATEGIC GROUP (ITEMS WITH SIGNIFICANT VALUE SMALLER THEN 0.09)

the three clusters differed on 8 out of 48 providers, as shownin Table XI. Proactive players had a strong emphasis on all 8

providers, building partnerships with suppliers, involving cus-tomers in product development, empowering people, adopting


TABLE XIAGILITY PROVIDERS BY STRATEGIC GROUP (SIGNIFICANT ITEMS AT 0.09 OR LESS)

advanced manufacturing technology (AMT), achieving total in-tegration, and using IS to support product development. This isin line with capabilities they emphasize. Quick and responsiveplayers had a low emphasis on the 8 providers comparatively.The two groups did not have much difference. Quick players hada stronger focus on the use of AMT and integration with sup-pliers while responsive players emphasized practices to trackchanges. The evidence to support the statement that the threegroups have distinct action plans was not strong.

To explore this further, a cluster analysis, similar to thatcarried out on agility capabilities, was carried out to identifygroups of companies emphasizing similar sets of action pro-grams. It was interesting to find that, in terms of action programsfor agility, there were three broad groups. Table XII showsthe results, where action programs on which the three groupssignificantly differ are listed. Group 1 puts more emphases onsupply chain related programs, partnering with suppliers/cus-tomers, involving suppliers/customers in product development,team working, etc. This group was named the “Supply ChainDeveloper.” Group 3 puts significant emphasis on technology,organization, IS, and integration, in particular, AMT, mass-cus-tomization, training people and vertical integration. They were

named “Integrator.” Group 2 did not have a focus. Centroidvalues on individual programs were low and were lower thanthose of at least one other group. They were named “No-goer”in the sense that they did not appear to have a focused actionplan. This suggests that manufacturers generally adopt threetypes of agility action plans, one emphasizing supply chaindevelopment, one technology and integration of technologywith people and organization, and one without a particularfocus. We’ve carried out a cross tabulation between strategygroup and the action plan group memberships (Table XIII). Achi-square indicates no significant association . Allstrategic groups had members employing each of the actionplans. We checked for possible agility strategies that mightarise from the action plans by comparing capability profiles ofthe three action plan groups using ANOVA. The differenceswere not significant. The existence of members in each strategygroup employing different action plans suggests that alternativeplans might exist for the delivery of each strategy type. Oursubsequent case studies, involving 14 firms from differentsectors and different strategy types, selected from 20 who hadindicated an interest in taking part in further studies, appear tosupport this.


TABLE XIIAGILITY PROVIDERS BY GROUP (SIGNIFICANT ITEMS AT 0.05 OR LESS)

The case studies found two typical plans corresponding toquick strategy, one to responsive strategy, and two to proactive

strategy. The first plan for quick strategy relates to firms whohave short product life cycles and derive revenues from the


TABLE XIIIPROPORTION OF NO-GOERS, INTEGRATORS, AND SUPPLY CHAIN DEVELOPERS IN AGILITY CAPABILITIES GROUP

early stages of product life cycles. The focus is on high speedof innovation, high speed of NPI and first to market. Theplan emphasizes innovation at all levels, patents/innovativeproducts, sufficient technology capability, AMT, mass cus-tomization, flexible teams and educated people, and integrationusing IS. The second plan concerns firms who have relativelylong product (line) life cycles and operate mainly at maturestages of product lines in a market where dominant competitionis delivery speed. The focus is on fast operation and quickproduct delivery. The action plan emphasizes close informa-tion integration with suppliers/customers, involving suppliersin product development and using mass customization. Theresponsive action plan corresponds to firms who have longproduct (line) life cycles and operate at the mature stage ofproduct lines, in a market where competition is multi-faceted.The firms are market followers that have to respond to customerdemands with wide varieties, small/variable volumes, and shortlead-times, and to competitions imposed by market leaders oncost, new product introduction, new technology, etc. The focusis on flexibility and being responsive to changes. The plan em-phasizes flexible manufacturing processes, flexible team-basedorganization, continuously trained people, concurrent organi-zation, and IS to support product development/manufacturingand integration with customers and suppliers. It also valuesinvolving customers in product development and collaboratingwith competitors. The first action plan for proactive strategyrelates to firms who share similar characteristics with respon-sive players but are market leaders instead (or well positionedin market). They focus on being quick, flexible and responsivesimultaneously to capture market niches, cope with globalcompetition, and deal with dynamic changes in customerrequirements, and on being proactive to introduce changes tomarket to maintain leaderships. The plan emphasizes almost allcapabilities emphasized by quick and responsive plans, seem-ingly consistent with the taxonomy in that proactive playerscompete along both “speed to customers” and “change profi-ciency” dimensions. In addition, the plan also emphasizes a listof action programs which may be identified with proactiveness,such as management styles that are informal, coaching andinspiring people, dynamic organization, empowering people,

involving employees at all levels in decision making, commu-nicating strategies, plans, problems, and new opportunities toemployees, performing proactive R&D concurrently with mar-keting, design and manufacturing. The plan also emphasizespartnership with suppliers and customers, reducing number ofsuppliers, and having suppliers audited, ranked and informed ofdecisions, which appear to relate to the partnership capability.The second action plan for proactive strategy relates to somesuppliers (often first tier) to major car/aerospace manufac-turers. They have few customers and competitors. The productlines are mature though improvements are made continuously.Product life cycles are decided by customers. For automotivesuppliers, pressures mainly come from customers’ drives forjust-in-time delivery, cost reduction, the reduction of number ofsuppliers, and faster introduction of new models. For aerospacesuppliers, products are characterized by high quality, highreliability, and high technology. Pressures are from customers’needs and wants in quality, cost, delivery, and new designs.Both scenarios require firms to partner with customers, beflexible, quick and responsive to meet customer requirements,and be proactive to work with customers in understanding newproducts, and feeding ideas to customers. The plan is similar tothe first action plan but emphasizes fewer practices on proac-tiveness and more on integration with customers, such as dataexchange with customers. It is interesting to note that theseplans focus on either supply chain development, integration, orboth, consistent with our clustering. Table XIV summarizes thethree agility strategies, their characteristics and business cases.

VII. SUMMARY AND CONCLUSION

A common theme that permeates manufacturing strategy lit-erature is that manufacturing task, as measured by the impor-tance given to competitive capabilities, must be linked to man-ufacturing choices, and that both should link to the businessstrategy. This theme does not consider capabilities of dealingwith changes in the business environment directly in a man-ufacturing strategy. The increasing rate of changes has how-ever severely impacted on the way manufacturing units operateand has highlighted the need to consider business environment


TABLE XIVTHE THREE AGILITY STRATEGIES AND CASES

change as a direct driver for manufacturing strategy. While theconcept of agility has been widely accepted in industry, howto incorporate agility in manufacturing strategy development isnot fully understood. This paper proposed a framework for de-veloping agility as a manufacturing strategy and conducted anempirical research into agility strategies in a number of U.K.

manufacturing sectors. The research has identified, through ataxonomical approach, three distinct types of agility strategiesand has explored how these strategies were shaped by changesin the business environment. Manufacturing choices employedby firms in different groups were also studied to find if they linkto manufacturing tasks.


Our taxonomy provides analytical evidence that generallysupports the work of Goranson [97] that hypothesized theexistence of three types of agility. Goranson’s “agility 1”features the ability to satisfy and be close to customers, “agility2” corresponds to the capability to thrive in changes that maybe anticipated, and “agility 3” refers to the ability to copewith unanticipated changes. Our quick players, competingon “customer focus” and “quickness,” appear to be operatingGoranson’s agility 1. Responsive players, emphasizing “re-sponsiveness to change” and “flexibility,” adopt a reactiveapproach to dealing with changes and appear to be com-peting along Goranson’s agility 2. Proactive players put strongemphasis on all capabilities but differentiate themselves byplacing high importance on “proactiveness to attack threats andopportunities” and “partnership.” This is a way of coping withchanges unanticipated so it agrees to some extent with agility3. However, there is a difference, in that proactive players alsoseek to create and benefit from changes.

The research also found that speed based agility was gen-erally coupled with customer focus, responsiveness withflexibility, and proactiveness with partnership and all other ca-pabilities. This was confirmed by the results of factor analysis.In addition, we’ve found two underlying dimensions separatingthe three groups. The first emphasizes the ability to cope withchanges, reactively or proactively. The second emphasizesthe ability to get close to and be quick to satisfy customers.Quick players mainly compete along the second dimension;responsive players along the first dimension, while proactiveplayers along both dimensions. Quick players, while sufferingfrom the least pressures across agility drivers, adopted a “satis-fying customers approach” through the development of strongcustomer focus and speed of operation. As pressures build up,in rapidly changing market/products, responsive players feltthe need to develop flexibility and responsiveness to changes.As pressures further build up in the above areas, as well asin “the rate of change in product models,” “effectiveness ofcompetitors’ strategies,” “delivery time and time to market,”“increasing value of information and services,” and technologyand social changes, proactive players felt the need to adopta proactive approach. In doing so, they spotted the need todevelop all agility capabilities and partner with other organiza-tions, suggesting that to be proactive, sufficient responsiveness,flexibility, quickness, and customer focus may be required.This implies that the way towards high agility may involvethe development of customer focus, quickness and flexibilityfirst, then responsiveness to changes, then being proactive andpartnering with others. This is in line with concepts proposedby others [69].

The three agility strategies appear to be generic across anumber of industrial sectors. Strong evidence was found tosuggest that the three strategies were shaped by pressuresfrom the business environment. For each strategy, evidenceswere also found to suggest that alternative action plans existto deliver the strategy. The choice of plans for each strategyseems to relate to the nature of market, the characteristics ofproducts (maturity stages) and firms’ market positions. Futurework is required to confirm this and to clarify the effectivenessof different plans to the delivery of agility strategies, and the

effectiveness of different strategies in coping with drivers. Alimitation of the research is that the sample was restricted to anumber of sectors in the U.K.. It will be interesting to find ifthe conclusions still hold for more sectors and in other parts ofthe world.

ACKNOWLEDGMENT

The authors would like to thank the two anonymous reviewersand the Department Editor Prof. J. Liker for their extremelythoughtful comments which have helped them to improve themanuscripts considerably.

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Zhengwen (David) Zhang (M’92–SM’04) receivedthe Ph.D. degree in manufacturing systems fromBrunel University, Uxbridge, U.K.

He is Professor and Chair of Manufacturing Sys-tems, Head of Advanced Technologies Research In-stitute (ATRI), and Director of Exeter Manufacturingand Enterprise Centre (XMEC) in the School of En-gineering, Computer Science and Mathematics, Uni-versity of Exeter, U.K. Prior to taking up the Chair ofManufacturing Systems at Exeter University in 2000,he worked as Lecturer at the University of Liverpool,

Liverpool, U.K., where he founded and led as Director of the Agile Manufac-turing and Enterprise Centre. His research interest, over the last 20 years, hascovered both technology and management aspects of manufacturing. His cur-rent interest focuses on manufacturing strategies, agile/lean manufacturing, dy-namically integrated manufacturing systems (DIMS), supply chain modelling,simulation and optimization, multiagent systems for manufacturing/logistics in-tegration, and complex systems involving socially interacting elements. He haswritten over 110 articles in refereed journals and conference proceedings.

Prof. Zhang serves as a Board Director of Southwest Manufacturing AdvisoryServices in England. He has consulted over 20 firms on manufacturing strategyand continuous improvement programmes.

Hossein Sharifi is a Lecturer in e-business and op-erations management at the University of LiverpoolManagement School, Liverpool, U.K. With a numberof years of industrial experience he completed aPh.D. degree study in manufacturing strategy (agilemanufacturing) at Liverpool University.

He is also an Associate of the Faculty of IndustrialEngineering at Iran University of Science and Tech-nology, Tehran, Iran. His research interests are in theareas of “lean and agile manufacturing and systemsincluding agile supply chain and operations manage-

ment, and “e-Business strategies, models and systems including e-governmentsystems.” He has led a few successful research projects including a pioneeringwork in the area of agile manufacturing as his Ph.D. study, and Alignment ofSupply Chains in the Liverpool University e-Business Research Centre. He iscurrently a grant holder in the U.K. collaborative project of Innovation and Pro-ductivity Grand Challenge and is leading a study into the U.K. University Tech-nology Transfer Offices. He has published widely in these areas some of whichhave received recognition and highly referenced.

towards theory building in agile manufacturing strategy—a taxonomical approach

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