enhancing enterprise agility by deploying agile drivers, capabilities and providers

Information Sciences 181 (2011) 3693–3708

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Information Sciences

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Enhancing enterprise agility by deploying agile drivers, capabilitiesand providers

Yi-Hong Tseng, Ching-Torng Lin ⇑Department of Information Management, Da-Yeh University, Chang-Hua, Taiwan

a r t i c l e i n f o

Article history:Received 6 November 2008Received in revised form 1 April 2011Accepted 26 April 2011Available online 3 May 2011

Keywords:Agile enterpriseFuzzy logicFuzzy agility indexOperational strategyQuality function deployment (QFD)

0020-0255/$ - see front matter � 2011 Elsevier Incdoi:10.1016/j.ins.2011.04.034

⇑ Corresponding author. Address: 112 Shan-Jiau RE-mail address: [email protected] (C.-T. L

a b s t r a c t

Agility is perceived as the dominant competitive vehicle for all organizations in an uncer-tain and ever-changing business environment. When embracing agility, important ques-tions must be asked. What precisely is agility and how can it be measured? How canone adopt the appropriate agile enablers to develop agility? How can one effectively assistin enhancing agility?

For an enterprise to achieve agility, it is critical to create an effective integrated proce-dure within the business that coordinates and ensures that the agility providers can satisfythe agility capabilities and cope with drivers, ultimately transforming all of these attributesinto strategic competitive edges. However, the existing literature on enterprise agility hasfailed to sufficiently address the relevant perspectives in such analyzes. The relationshipmatrix in the quality function deployment (QFD) method provides an excellent tool fordeploying important concepts and linking processes. This report suggests a new agilitydevelopment method for dealing with the interface and alignment issues among the agilitydrivers, capabilities and providers using the QFD relationship matrix and fuzzy logic. Afuzzy agility index (FAI) for an enterprise composed of agility capability ratings and a totalrelation-weight with agility drivers was developed to measure the agility level of an enter-prise. This report also describes how this robust approach has been applied to develop agil-ity in a Taiwanese information technology (IT) product and service enterprise. Thisdevelopment project revealed that the proposed framework and procedures can enhancethe agility of an enterprise as well as ensure a competitive edge.

� 2011 Elsevier Inc. All rights reserved.

1. Introduction

At the beginning of the twenty-first century, the world faces profound changes in market competition, technological inno-vation and customer demand. The world-wide growth in education and technology has led to intense and increasingly globalcompetition and an accelerated rate of innovative change in the marketplace. There is a continuing fragmentation of massmarkets into niche markets, as customers become more demanding with increasing expectations. This critical situation hasled to major revisions in business priorities, strategic vision and the viability of the conventional and relatively contemporarymodels and methods [41]. To cope with these changing competitive markets, as well as the ability to meet customer demandfor increasingly shorter delivery times, it is critically important to ensure that supply can be synchronized to meet the peaksand troughs of the demand [1,53]. Companies now require a high level of maneuverability encompassing the entire spectrumof activities within an organization. Agility in addressing new ways to manage enterprises for quick and effective reaction to

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3694 Y.-H. Tseng, C.-T. Lin / Information Sciences 181 (2011) 3693–3708

changing markets, driven by customer-designed products and services, has become the dominant vehicle for competition[56].

Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment andwith making an appropriate response. Therefore, these enterprises require a number of distinguishing attributes to promptlydeal with the changes within their environment. Such attributes consist of four principal elements [43,54]: responsiveness,competency, flexibility/adaptability and quickness/speed. The foundation for agility is the integration of information tech-nologies, personnel, business process organization, innovation and facilities into strategic competitive attributes.

The embracing of agile strategies has several benefits for companies, including quick and efficient reaction to changingmarket requests, the capability to customize products and services delivered to customers, the capability to produce and de-liver new products in a cost-efficient manner [45], decreased manufacturing costs, increased customer satisfaction, removalof non-value-added activities and increased competitiveness. Accordingly, agility has been advocated as the business para-digm of the 21st century. Agility is considered the winning strategy for becoming a global leader in an increasingly compet-itive market of quickly changing customer requirements [2,17,54]. Some researchers [4,11] further claimed that agility is thefundamental characteristic for survival and competitiveness. However, the ability to develop and measure agility has notbeen built as rapidly as anticipated, because the technology for managing and enhancing enterprise agility is still beingdeveloped [43,56]. Thus, in embracing agility, many important questions must be asked, such as: What precisely is agility?What methodology exists that can provide firms with the means to develop entrepreneurial agility? What are the appropri-ate agility enablers? How should the appropriate agile enablers be adopted? How can agility be measured? How will com-panies know when they possess this attribute because no simple metrics or indices are available? How and to what degreedo the attributes of an enterprise affect its business performance? How does one compare agility with a competitive enter-prise? To improve entrepreneurial agility, how does one identify the principal unfavorable factors? How can one assist inmore effectively achieving agility [20,43]? Answers to such questions are critical to practitioners and the theory of agileentrepreneurial design. The purpose of this research is therefore to seek solutions to some of these problems with a partic-ular focus on agile development planning and measurement, as well as identifying the principal obstacles to improvingagility.

The purpose of agile development planning is to unite the resources of an enterprise to create business value. Accordingto previous studies [3,6,9,49], the value of a firm can be maximized and competitive threat minimized only by selecting astrategy based on all facets of the business, such as new-market requirements as well as competitive and operation strate-gies, not merely as isolated organizational strategy islands within the company [37]. Thus, for a successful organizationalstrategy such as enterprise agility, it is critical to create an effective integrated procedure within the business to ensure thatthe agility providers can satisfy the agility capabilities and cope with agility drivers, ultimately transforming all of theseattributes into strategic competitive edges [33,57]. Although alignment among competitive drivers, agility capabilitiesand providers are all very critical in making an enterprise agile, it is difficult for an enterprise to achieve agility becauseof the lack of an efficient approach for agile development planning. The focus of recent research [15,18,38,41,42] has beenon setting enterprise agility. However, there is a lack of sufficient details on how these objectives are translated into actionplans. These approaches do not deal directly with the interface and alignment issues among competitive drivers, agility capa-bilities and the choice of appropriate agility providers.

The relationship matrix in the quality function deployment (QFD) method provides an excellent tool for aligning impor-tant concepts and linking processes. Fuzzy logic is a useful tool for capturing the ambiguity and multiplicity of linguisticjudgments required to express both the relationships and ratings of agility attributes. To compensate for the lack of an effi-cient approach, which can deal directly with the interface and alignment issues among competitive drivers, agility capabil-ities and the choice of appropriate agility providers to assist managers in more efficiently achieving agility, a new systematicmethodology for agile development planning, based on fuzzy logic and the relationship matrix in the QFD, is devised to alignthe overall relationship from the agility drivers in the business environment down to the agility providers (with guidanceand direction for realizing enterprise agility). A fuzzy agility index (FAI) for an enterprise is developed for measuring the agil-ity of an enterprise as well as for identifying the principal obstacles for improving agility. As an illustration, this report dem-onstrates how the proposed approach was applied to develop agility in a Taiwanese information technology (IT) product andservice enterprise.

The remainder of this report is organized as follows. Section 2 reviews the related research. Section 3 discusses fuzzy logicand its applications in decision making. A conceptual model of an agile enterprise is described in detail for developing a sys-tematic evaluation methodology in Sections 4 and 5. A practical case is presented in Section 6. Section 7 presents concludingremarks and suggestions.

2. Review of related research

2.1. Methodology for enhancing agility

Numerous conceptual models for agility implementation have been proposed to assist managers in enhancing agility. Forexample, to promote a new understanding of cooperation as a vital means of survival and prosperity in the new business era,Preiss et al. [38] developed a generic model for agility. The first integrated framework to achieve agility was proposed by

Y.-H. Tseng, C.-T. Lin / Information Sciences 181 (2011) 3693–3708 3695

Gunasekaran [15]. This framework illustrated how the major capabilities of agile manufacturing, such as ‘‘co-operation’’, ‘‘va-lue-based pricing strategies’’, ‘‘investments in people and information’’, and ‘‘organizational changes’’, should be supportedand integrated with appropriate agile enablers to develop an adaptable organization. Furthermore, in seeking to exploit theconcept and practices of agility, four research teams [4,18,41,56] developed a three-step methodology for achieving agility.The model specifically links agility ‘‘drivers’’ (i.e. changes in a business environment that pressure companies to embrace theagile strategy) to four essential agile capabilities. In the last step, a set of enabling factors (agile ‘‘providers’’) for achievedcapabilities, is described and linked to the previously mentioned capabilities. Although frameworks for formulating agilityhave been identified, most of them are structural in nature. Thus, to ensure that the providers can satisfy the strategic direc-tion of an enterprise, a suitable integrated methodology to enhance agility by identifying its providers, beginning with thecompetitive basis of the enterprise, is critical to both practitioners and the theory of agile enterprise design.

2.2. Measurement

Several scientific methods dealing with the measurement of agility have been proposed to assist managers in assessment.However, most of these methods only assess the capabilities of agility. Some authors [11,48,52,55] have defined an agilityindex as a combination of enabling attribute intensity level measurements. Thus, they suggested assessing agility using aweighted index. Other measurement methods [31,39] have been developed based on an analytical hierarchical process(AHP). An evaluation index for customization agility in mass production manufacturing was devised by Yang and Li [50]. Thisindex is computed as a weighted sum of a company’s performance against its agile capabilities, weighting the relative impor-tance of the agile capabilities. To overcome the vagueness of agility assessment, Tsourveloudis and Valavanis [46] designedsome IF-THEN rules based on fuzzy logic. A mass customization manufacturing agility evaluation approach based on theTOPSIS concepts was proposed by Wang [47] by analyzing the agility of organizational management, product design, pro-cessing manufacture, partnership formation capability and information system integration. Using fuzzy association rule min-ing, a new approach for evaluating agility with both tangible and intangible attributes was developed by Jain et al. [19] tosupport managers in enhancing firm flexibility. Each of these techniques appears to only address a limited aspect of a verycomplicated problem. Although each technique contributes to an understanding of the problem, each functioning alone isinsufficient to handle the problem in its entirety because the capabilities and assessment should be closely linked and coor-dinated with the drivers and the providers [23]. It is therefore necessary to examine the problem from a broader perspective.

2.3. QFD relationship matrix

The QFD method was designed to emphasize detailed pre-planning to meet customer needs and the requirements for newproduct development. It employs several charts, called the house of quality (HOQ), to translate customer desires into theproduct design or engineering characteristics and subsequently into the characteristics of the parts, process plan and pro-duction requirements related to product manufacturing. The basic HOQ format consists of seven major components: (1) cus-tomer requirements (CRs), (2) importance of customer requirements, (3) design requirements (DRs), (4) relationship matrixfor CRs and DRs, (5) correlation among DRs, (6) analysis of competitors, and (7) prioritization of design requirements.

Although QFD was proposed for a customer-driven product development and delivery methodology, an enterprise canachieve various corporate strategic goals using the QFD approach [24,34]. QFD can be extended for the alignment of agilitydrivers, capabilities and agility providers to enhance agility and make priority decisions concerning specific providerimprovements that should be made to enhance the agility of an enterprise. This study uses a simplified HOQ matrix, in whichthe competitive analysis of competitors and correlation analyzes among DRs are removed. This simplified matrix is called arelationship matrix, in which CRs are represented on the left side. Identifying the relative importance of the various CRs is animportant step in discerning those that are critical and also helps to prioritize the design effort. The relative importance ofthe DRs, represented in the upper portion of the relationship matrix can be calculated using the relative importance of theCRs and the level assigned to the relationships between the CRs and DRs, presented in the main matrix body. This can berepresented in symbolic or numerical form. The level of the relationships is typically assessed using an evaluation team.

3. Fuzzy logic and applications in decision making

3.1. Linguistic variables

The linguistic variable concept is very useful when dealing with situations that are too complex or too ill-defined to bereasonably described in conventional quantitative expressions. A linguistic variable is a variable whose values are words orsentences in natural or artificial language. For example, ‘low’ is a linguistic variable if its value is linguistic rather thannumerical. Using the approximate fuzzy-set theory reasoning, linguistic values can be represented by fuzzy numbers. Forexample, the fuzzy numbers approximating the linguistic weighting values for the linguistic variables {extremely low, verylow, low, fair, high, very high, extremely high} are listed in Table 1. Although there are many forms of fuzzy numbers that canrepresent linguistic values, triangular fuzzy numbers are used here because they can be easily specified by experts. Undercertain weak assumptions such use immediately complies with the relevant optimization criteria [36].

Table 1Linguistic variables and their corresponding fuzzy numbers for assessing.

Change-levels Merit ratings Relationship-levels

Linguistic variable Fuzzy number Linguistic variable Fuzzy number Linguistic variable Fuzzy number

Extremely low (EL) (0,0.05,0.15) Worst (W) (0,0.05,0.15) Very low (VL) (0,0.1,0.2)Very low (VL) (0.1,0.2,0.3) Very poor (VP) (0.1,0.2,0.3) Low (L) (0.1,0.25,0.4)Low (L) (0.2,0.35,0.5) Poor (P) (0.2,0.35,0.5) Fair (F) (0.3, 0.5,0.7)Fair (F) (0.3,0.5,0.7) Fair (F) (0.3,0.5,0.7) High (H) (0.6,0.75,0.9)High (H) (0.5,0.65,0.8) Good (G) (0.5,0.65,0.8) Very high (VH) (0.8,0.9,1.0)Very high (VH) (0.7,0.8,0.9) Very good (VG) (0.7,0.8,0.9)Extremely high (EH) (0.85,0.95,1.0) Excellent (E) (0.85,0.95,1.0)


3.2. Fuzzy weighted-average

The fuzzy weighted-average is an integrated information measure that consolidates fuzzy ratings and weightings for allfactors being measured. Thus, it represents the overall merit or attractiveness of a given object. Several methods have beendevised for calculating fuzzy weighted-averages [21,26]. The fractional programming approach developed by Kao and Liu[21] is adopted to efficiently compute the fuzzy weighted average.

Let R1,R2, . . . ,Rn and W1,W2, . . . ,Wn denote the fuzzy ratings and the fuzzy importance weights for the criteria. With ncriteria, the fuzzy-weighted average of Ri and Wi is defined as

Y ¼Xn

i¼1

WiRi

Xn

i¼1

Wi

,; ð1Þ

Let t ¼ 1Pn

i¼1Wi�

and vi= twi, according to the fractional programming approach. The lower and upper bounds of the specifica-cut of Y can be solved as

YLa ¼min y ¼

Xn

i¼1

v iðRiÞLa;

s:t: tðwiÞLa 6 v i 6 tðwiÞUa ; I ¼ 1; . . . ;n;Xn

i¼1

v i ¼ 1;

t; v i=0;

ð2aÞ

YUa ¼max y ¼

Xn

i¼1

v iðRiÞUa ;

s:t: tðwiÞLa 6 v i 6 tðwiÞUa ; I ¼ 1; . . . ;n;Xn

i¼1

v i ¼ 1;

t; v i=0:

ð2bÞ

By enumerating different a values, the membership function Y can be constructed.

3.3. Fuzzy ranking method

Fuzzy numbers do not always yield a totally ordered set as real numbers do. Many fuzzy ranking methods for comparingfuzzy numbers have been developed to resolve this problem [8,27]. Here, the fuzzy number ranking is based on Chen andHwang’s left-and-right fuzzy ranking method [8] because it preserves the ranking order and also considers the absolute loca-tion of each fuzzy number. The shortcoming of this method is that the ranking score depends on the definition of the fuzzymaximizing and minimizing set.

In this ranking method, the fuzzy maximizing and minimizing sets are defined as:

fmaxðxÞ ¼x; 0 6 x 6 1;0; otherwise;

�ð3Þ

fminðxÞ ¼1� x; 0 6 x 6 1;0; otherwise;

�ð4Þ


when given a triangular fuzzy number M defined as: fM : R ? [0,1] with a membership function defined as:

fMðxÞ ¼ðx� aÞ=ðb� aÞ; a 6 x 6 b;

ðx� cÞ=ðc � bÞ; b 6 x 6 c;

0; otherwise:

8><>:

The right score for M can then be obtained as

SRðMÞ ¼ supx½fMðxÞ ^ fmaxðxÞ�: ð5Þ

The left score for M can be obtained as

SLðMÞ ¼ supx½fMðxÞ ^ fminðxÞ�: ð6Þ

Finally, the total scores for M can then be obtained as

STðMÞ ¼ ½SRðMÞ þ 1� SLðMÞ�=2: ð7Þ

3.4. Fuzzy logic applications in decision making

Fuzzy logic enables one to effectively and efficiently quantify imprecise information, perform reasoning processes andmake decisions based on vague and incomplete data [30]. Roussel et al. [40] suggested that the experts can manage the riskwhen it is known, but in uncertain situations when available information is scarce or unreliable or when the target objectivesand goals are not clearly defined, managers often react very poorly. By making no global assumptions about the indepen-dence, exhaustiveness, or exclusiveness of the underlying evidence, fuzzy logic tolerates a blurred definition boundary[30]. Thus, fuzzy logic seeks to incorporate qualitative factors into decision making.

Fuzzy logic is currently being used extensively in many industrial applications such as water treatment, travel timereduction, subway systems, washing machines, vacuum cleaners, rice cookers and aircraft flight control [51]. Fuzzy logichas also been applied to managerial decision making as well. For example, it has been used in developing an industry attrac-tiveness-business strength matrix for strategic portfolio selection [12]. Lin and Chen [29] devised a fuzzy-possible-success-rating for evaluating go/no-go decisions for new-product screening based on the product marketing of competitive advan-tages, superiority, technological suitability and risk. To help organizations build awareness of the critical influential factors inknowledge management implementation using the fuzzy multi-criteria decision making approach, Chang and Wang [7]developed a fuzzy success probability index to provide a check mechanism for developing knowledge management. Becausecash flow cannot be estimated precisely in an uncertain decision making environment and the values of managerial flexibil-ities cannot be exactly revealed through discounted cash flow analysis, by integrating the discounted cash flows and internalrate of return analysis, Liao and Ho [28] proposed a fuzzy binomial approach for strategic investment project valuation. Hsuet al. [16] integrated a fuzzy linguistic decision model with a genetic algorithm to extract the optimal promotion mix for avariety of tools while satisfying the expected marketing performance and budget limitations and applying them to deter-mine the most appropriate mix of promotion tools.

4. Agile enterprise conceptual model

The goal of an agile enterprise is to enrich/satisfy customers and employees. An enterprise essentially possesses a set ofcapabilities for making appropriate responses to changes occurring in its business environment. However, the business con-ditions in which many companies find themselves are characterized by volatile and unpredictable demand. Therefore, anincreased urgency exists for pursuing agility. Agility might, therefore, be defined as the ability of an enterprise to respondrapidly to changes in the market and customer demands. To be truly agile, an enterprise should possess a number of distin-guishing agility-providers. By reviewing the relevant literature [18,38,41,45,56], the author has developed an agile enterpriseconceptual model, as shown in Fig. 1.

The main driving force behind agility is change. There is nothing new about change. However, change is currently occur-ring at a much faster rate than ever before. Turbulence and uncertainty in the business environment have become the maincauses of failures in enterprises. The number of changes and the types of change, and the specification or characteristics can-not easily be determined and the probability is indefinite. Different enterprises with dissimilar characteristics and circum-stances experience various changes that are specific and perhaps unique to them. However, there are some commoncharacteristics in the changes that occur, which can produce a general consequence for all enterprises. By summarizingprevious studies [41,43,54,56], the general areas of change in a business environment can be categorized as: (1) marketvolatility caused by growth in the market niche, increasing the introduction of new products and product life; (2) intensecompetition caused by rapidly changing markets, pressure from increasing costs, international competitiveness, Internetusage and a short development time for new products; (3) changes in customer requirements caused by demands for

Agility capabilities: Responsiveness

Competency

Flexibility

Quickness

Agile-enterprise goals:

Enrich and satisfy customers

Cost

Time

Function

Robustness

Agility drivers (Changing competition in business environments)

Markets

Technological

innovations

Social factors

Customers’

requirements

Competition criteria

Agility providers/pillars

Process

integration

(foundation

Collaborative

relationships

(strategy)

Information

integration

(infrastructure)

Customer/marketing

Sensitivity

(mechanism)

Fig. 1. Conceptual model of an agile enterprise.


customization, increased expectations about quality and a quicker delivery time; (4) accelerating technological changescaused by the introduction of new and efficient production facilities and system integration; and (5) changes in social factorscaused by environmental protection, workforce/workplace expectations and legal pressure.

Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment andwith making appropriate responses. Therefore, such enterprises require a number of distinguishing capabilities, or ‘‘fitness,’’to deal with these concerns. These capabilities consist of four principal elements [43,54]: (1) responsiveness, the ability tosee/identify changes, to respond quickly, reactively or proactively and to recover; (2) competency, the efficiency and effec-tiveness of an enterprise in reaching its goals; (3) flexibility/adaptability, the ability to implement different processes andachieve different goals with the same facilities; and (4) quickness/speed, the ability to culminate an activity in the shortestpossible time.

Achieving agility requires responsiveness in strategies, technologies, personnel, business processes and facilities. Agility-providers should exhibit agile characteristics as well as make them available and determine the agility capabilities andbehavior of an enterprise. Numerous studies have been conducted that were dedicated to identifying agility-providers fromwhich organization leaders can select items appropriate to their own strategies, organizational business processes andinformation systems [13,25,35,54,56]. Yusuf et al. [54] proposed a set of thirty-two agility-providers grouped into fourdimensions: (1) core competency management, (2) virtual enterprise, (3) capability for reconfiguration, and (4) knowl-edge-driven enterprises. These attributes, representing most aspects of agility, determine the entire behavior of anenterprise. Most recently, in a review of enterprise agility conducted by Sherehiy et al. [44], seven principal componentscomprising thirty-five attributes were identified as the critical characteristics/attributes of an agile enterprise: (1) flexibilityand adaptability, (2) responsiveness, (3) speed, (4) integration and low complexity, (5) mobilization of core competences, (6)high quality and customized products, and (7) culture of change. From this review we can see that different researchers pro-vide various insights into different aspects of agility providers. It is highly probable that there is no single set of agilityproviders reflecting all aspects.

Although several researchers [15,18,38,41,12] have accepted a conceptual model for enhanced agility, the purpose of agiledevelopment planning is to unite the resources of an enterprise to compete with the changes in the environment and tocreate business value, which according to some studies [23,56] can be maximized and the competitive threat minimized onlyby selecting appropriate agility providers for investments aligned to the company’s business strategy and competitive basesin the market. Thus, the first priority should be to understand the relationships among the specific market field require-ments, as well as the agility capabilities and providers, to align and integrate both capabilities and providers with agilitydrivers and to transform them into a competitive edge. However, no tool exists that provides integrated methodologies


suitable for organizations to deal directly with the issues of coordination and alignment among competitive drivers in therelated market, agility capabilities and choice of appropriate agility providers.

To assist managers in more efficiently achieving agility, using the agile enterprise conceptual model and the relationshipmatrix in the QFD approach, an original systematic model for aligning and integrating to ensure that the agility providers cansatisfy the agility capabilities and cope with the agility drivers can be constructed as shown in Fig. 2. This model is describedas follows:

� Aligning agile strategy: to identify the degree of agile abilities that can provide the required strength for responding tochanges and searching for competitive advantage using the close linkage and coordination of agility drivers and agileabilities.� Aligning agile ingredients: to find agility providers consisting of the means by which the so-called needs of an enterprise

are related to the capabilities that can be achieved by close linkage and coordination of abilities and providers.

5. A fuzzy QFD-based algorithm for evaluation of agility

As mentioned in the previous section, for the alignment and integration among agility drivers, capabilities and providers,it is critical to ensure that the agility providers can satisfy the agility capabilities and cope with agility drivers and theirtransformation into a competitive edge. Due to an ‘‘imprecise’’ or ‘‘vague’’ definition of agile attributes and matrix relation-ships, the aligning and integrating evaluation process is associated with uncertainty and complexity. Managers must make adecision by considering agile attributes and relationships that might have non-numerical values. All attributes must be inte-grated within the evaluation decision although none of them may exactly satisfy the ideals of the enterprises. Conventional‘‘crisp’’ evaluation approaches cannot handle such evaluations suitably or effectively. Since humans have the ability tounderstand and analyze obscure or imprecise events that are not easily incorporated into existing analytical methods, thecorporate strategic planning evaluation is made primarily based on the opinions of experts. According to previous research[22], in situations where evaluators are unable to make a significant assessment, linguistic expressions are used to estimateambiguous events. Linguistic terms usually have vague meanings. One way to capture the meanings of linguistic terms is touse the fuzzy-logic approach to associate each term with a possibility distribution [10].

To assist managers in more efficiently enhancing agility using the QFD relationship matrix and fuzzy logic, a fuzzy logic-QFD agility-enhancing model (FLQFDAEM) composed of four major parts (as shown in Fig. 3) was devised for agility devel-opment and evaluation. The agility drivers are first identified based on a survey of the business operation environment. Theagility-level needs are then determined and the requirements for measuring the capabilities identified. The required provid-ers for the assessment are then selected. The relationship matrix is then aligned to analyze the fuzzy average relation-weightamong the drivers, capabilities and providers. The fuzzy ratings and average relation-weights of the capabilities are synthe-sized to obtain the fuzzy-agility-index (FAI) of the enterprise and to match the FAI with an appropriate linguistic term tolabel the agility level. The fuzzy ratings and average relation-weights of the providers are then synthesized to obtain the fuz-zy merit-relation-value index for each. They are ranked to identify the major barriers to enabling proactive implementationof the appropriate ameliorating measures.

6. A practical case study

The agility development project at the Electronic A (EA) Company is described to illustrate the details of the FLQFDAEMand demonstrate how it can be used in agility development. It is generally recognized that every firm has its own businessoperating strategy. Our attempt here is to present a generalized model based on past studies that can then be modified orextended for use in a specific situation or company.

Matrix for aligning agile strategies Matrix for aligning agile ingredients

Agility

capabilities

Agility

providers

Relationship

matrix

Relative

importance

Agility

capabilities

Relative

importance

Relationship

matrix

Agility

drivers

Fig. 2. Framework for aligning agility drivers, capabilities and providers.

Fig. 3. Framework for evaluating enterprise agility.


6.1. Subject of case study

The EA Company is an internationally recognized IT products-and-services company, particularly noted for PCs and note-books, earning an annual revenue of about US $7.37 billion in 2007. EA employs marketing and service operations across theAsia–Pacific Rim, Europe, the Middle East and the Americas, supporting dealers and distributors in more than 100 nations. Inthe 1990’s, the markets for IT products matured. Low-cost production in developing nations grew, thus prompting large mul-tinational firms to simultaneously provide local responsiveness and global integration in reaction to an uncertain businessenvironment. Such changes profoundly challenged the EA enterprise. To achieve and sustain global success and satisfy newsmall-niche markets, EA strived to become a major global supplier to enrich its customers, reduce time-to-market, reducethe total cost of ownership and enhance overall competitiveness.

Because an agile supply chain has been advocated as the 21st-century operation paradigm, being perceived as a winningstrategy to become a national and international leader, the corporate management team (executive team) concluded that itwished to achieve an extremely agile supply chain through the continuous improvement of processes. An assessment teamled by the executive vice president was organized. This team was selected from the most knowledgeable personnel who hadmastered the principles of an agile supply chain and whose job it was to investigate and correct problems. The teammembership comprised the vice president of marketing, the general auditor, the global supply chain manager, the director


of human resources, a senior project manager and two business operational strategy consultants. Each member brought par-ticular concerns and desires into the evaluation, which had to be reconciled by consensus, a necessary procedure since allparties would contribute to the success or failure of the project.

6.2. Project commitments

The aim of the agility development project was to align and integrate among agility drivers, capabilities and providers toensure that the agility capabilities could cope with the agility drivers and the agility providers could satisfy the agility capa-bilities, and to produce a good set of results, from which an agility index could be determined for perceptions of the currentsituation, with another index for the goals toward increasing the supply chain agility. Since top-level commitment is essen-tial, specific objectives for the development project were agreed on by the CEO:

� To implement an enterprise-wide self-assessment for establishing a baseline;� To identify the strengths of the supply chain and areas needing improvement for feedback to the management team;� To feed opportunities for improvement into the business operational planning cycle, including corporate objectives; and� To develop the process of self-assessment using the agile enterprise model as an annual component of the business cycle.

6.3. FLQFDAEM application to the EA agility development project

When EA set the goal to implement an agile supply chain, the committee had several questions, such as: What precisely isagility and how can it be measured? How can both analytical and intuitive understandings of agility be developed in a par-ticular business environment? How can an EA Company’s agility be enhanced? Answering these questions requires knowl-edge of what to measure, how to measure it and how to evaluate the results. Moreover, the way to link drivers, thecapabilities and providers to ensure that the agility providers can satisfy the agility capabilities and the agility capabilitiescan cope with the agility drivers, and their transformation into a competitive edge, must be taken into account if the supplychain is to implement agility. Although important concepts and steps in development have previously been identified, thereis still no systematic tool to unite these concepts. Furthermore, due to the existing ill-defined and ambiguous elements con-cerning agility attributes and their interrelationships, experts can easily differentiate between high, medium and low. How-ever, it is difficult to judge whether a value (e.g., 0.2) is low or another value (e.g., 0.3) is also low. Therefore, it is easier to uselinguistic terms to measure ambiguous events. The CEO expressed a desire to pursue a method that takes into account theuncertainty of each attribute yet maintains the nature of multiplicity to provide an overall picture of the possible agility rat-ing for the supply chain. Because linguistic variables contain ambiguity and a multiplicity of meanings and the informationobtained can be expressed as a range in a fuzzy set instead of as a single value like in traditional methods, fuzzy logic may beapplied in this evaluation context. Based on the fuzzy QFD-based evaluation model procedures, the agility development pro-ject was implemented and the goal achieved. The deliberations concerning how to initiate agility development are summa-rized below:

(1) Identify agility drivers, determine capabilities and select providers for assessment. The deployment and linkage ofagility drivers, capabilities and providers, and their transformation into a competitive edge is critical for enhancingagility. Because the situation varies from company to company, there is a high probability that no single set of factorsreflects all situations and requirements. Furthermore, evaluators with different functional perspectives bring particu-lar needs and desires into the evaluation. To accurately elicit assessment criteria reflecting the entire set of agile sup-ply chain features, the committee made a series of business-environment changes, as well as trend surveying andanalysis within a period of ten days. The major content included: changes in the marketplace, competitive circum-stances and criteria; technological innovations and applications; changes in customer requirements; and changes insocial factors. To facilitate the experts’ holistic understanding of the current situation, two review meetings were heldto discuss a series of activities, the major content of which included:
� Supply chain characteristics: supply chain priorities (quality, cost, time, customers satisfaction, etc.), perceived
quickness, responsiveness, core business and competencies, as well as specific supply chain problems;� Policy and strategy: the key factors prompting the supply chain to change and the goals and strategies adopted;� Business structure: organization, process, personnel, information technology and innovative structures providing

the capability for enhancing agility;� Practices: those performed in response to change.
Based on the discussion results, the committee further referred to the factors proposed in previous studies[31,41,43,48,54–56]. Delphi iterative procedures were used to facilitate a consensus on the selection of different cri-teria and their relative importance to the enterprise. The agility drivers were then identified and the capabilities andproviders for assessment selected, as shown in Table 2. (This Table presents only what the author assessed to be themost prevalent and meaningful factors for this case study).
(2) Determine the preference scale for measurement. The ad hoc usage of linguistic terms and corresponding membershipfunctions is characteristic of fuzzy logic. It is notable that many popular linguistic terms and corresponding member-ship functions have been proposed for linguistic assessment [8,22]. For the sake of convenience, instead of eliciting

Table 2Agility drivers, capabilities and providers for deploying and measuring agility index.

Drivers Capabilities Providers

Growth of niche market (A D1) Sensing/identifying changes and fast response(AC1)

Multi-skilled and flexible personnel (AP1)

Increasing rate of change in product models (A D2) Strategic vision (AC2) Workforce skill upgrade (AP2)Product lifetime shrinkage (A D3) Technological ability and appropriate product

introduction (AC3).Quick new product introduction (AP3)

Rapidly changing market (A D4) Cost-effectiveness (AC4) Response to changing marketrequirements (AP4)

Increasing pressure on cost (A D5) Cooperation and operation efficiency andeffectiveness (AC5)

Products with substantial value-addition(AP5)

Increasing pressure of global market competition(A D6)

Product volume/model flexibility (AC6) First-time right design (AP6)

Decreasing new products time to market (A D7) Organization/personnel flexibility (AC7) Trust-based relations with customers/suppliers (AP7)

Quicker delivery time and time to market (A D8) Product/service design, delivery alacrity andtimeliness (AC8)

Technology awareness (AP8)

Increasing quality expectation (A D9) Fast operation time (AC9) Skill and knowledge enhancement (AP9)Introduction of new soft technologies (software and

methods) (A D10)Concurrent execution of activities (AP10)

Environmental pressures (A D11) Information technology andcommunication (AP11)Empowerment and decentralizeddecision-making (AP12)Cross-functional team (AP13)Culture of change (AP14)


linguistic terms and corresponding membership functions from the experts, they could be obtained directly from pastdata or basic models that can be modified to incorporate individual situations and the requirements of different users.Due to limited short-term memory capacity it is suggested that the number of linguistic levels not exceed nine [32].As the assessment proceeded the committee members further investigated the supply chain characteristics, policy andstrategy, structure, the organization’s capabilities, its competition and the agility-related practice information anddata. The experts were unable to reach a consensus on linguistic variables and membership functions at first. To limitdebate and argument, the linguistic terms and corresponding membership functions used in previous studies wereadopted and modified to incorporate the specific requirements of the EA Company. To validate that these linguisticvariables and the membership functions were appropriate and to ease communications within the committee, weasked each of the seven evaluators to describe the membership functions when we gave them a linguistic variable.This continued until their answers reached a consensus. After two days of discussion, based on a long-standing rec-ognition of the meaning of linguistic values, the committee selected linguistic terms and their associated membershipfunctions for assessment, as listed in Table 1.

(3) The relationship matrix was applied and linguistic terms were used to assess the agility attributes and relationship-levels. The linguistic terms were translated into membership functions. Once the linguistic variables and their asso-ciated membership functions were defined, a series of brainstorming sessions were held over five days to identifythe relationships among the variables. The experts were asked about the mutual relationships among variables(e.g., how a particular variable helps to enhance the others). Using the conclusions in the review meetings and brain-storming sessions, and based on their experience, knowledge and judgment, the committee members applied the rela-tionship matrix (as shown in Tables 3 and 4) and used the level scale W = {extremely low [EL], very low [VL], low [L],fair [F], high [H], very high [VH], extremely high [EH]} to measure the degree of change in the agility drivers. They usedthe value scale RS = {very low [VL], low [L], fair [F], High [H], very high [VH]} to evaluate the strength of the relation-ships between the agility drivers and capabilities, as well as between capabilities and providers. They used the ratingscale R = {worst [W], very poor [VP], poor [P], fair [F], good [G], very good [VG], excellent [E]} to assess the merit ratingsof the capabilities and providers. A sample of the linguistic assignment is shown in Tables 3 and 4. Based on the asso-ciated relationships shown in Table 1, membership functions to approximate linguistic variable values, the linguisticassignments assessed by each expert were translated into membership functions.

(4) Analyze the fuzzy average relation-weight in the relationship matrix. It is important to aggregate the different expertopinions in group decision-making. Many methods can be used to aggregate expert assessments, such as the mean,median, maximum, minimum and mixed operators. Because the median operation is more robust in a small sample,this method was chosen to pool the experts’ assessments. Thus before this analysis, the committee used the medianoperation to integrate the different assignments under the same factors given by different experts.Based on the traditional QFD methodology [5] and the fuzzy weighted average definition [21,26], the fuzzy averagerelation-weight representing the total relationship-levels between a particular column item and the entire list ofrow items can then be defined as

Table 4Agility providers connected with agility capabilities: agile ingredient deployment matrix (assigned by general auditor).

Agility providers

AP1 AP2 AP3 AP4 AP5 AP6 AP7 AP8 AP9 AP10 AP11 AP12 AP13 AP14

Merits of agility providers VG G VG F G G G VG VG G G E F G

Agility capabilitiesAC1 H H VH H H H L H H F H VH H HAC2 H F H H H H VH H F H H H F VHAC3 VH VH VH H H VH F H H H VH F H HAC4 H H H H H H VH H H H H F F HAC5 H H H VH H VH H H H VH H H VH HAC6 VH H H VH H H H H H H H H H FAC7 VH H H VH H H H H H VH H VH H HAC8 H H H VH H VH H H H VH H H H HAC9 VH H H VH H VH H H H VH H H VH H

Table 3Agility capability against with drivers: agile strategies deployment matrix (assigned by general auditor).

Agility capabilities

AC1 AC2 AC3 AC4 AC5 AC6 AC7 AC8 AC9

Change-level Merits of agility capabilities

G G G F F VG F P F

Agility driversAD1 VH VH H H F F VH H H VHAD2 VH H H VH H H H VH H VHAD3 VH H H VH H H F H H VHAD4 VH H VH H H H VH H VH VHAD5 EH H F H VH H H F H HAD6 VH H VH H VH VH H VH VH VHAD7 VH VH H H F H H H VH VHAD8 VH VH H H F H H H VH VHAD9 H H F H F VH F F H FAD10 H H H H H F H H H HAD11 H F H F L F L F F L


FARWACJ ¼Xm

i¼1

ðFRLADACij � FLCADiÞXm

i¼1

FLCADi

,; ð8Þ

where FARWACj denotes the fuzzy average relation-weight of the jth agility capability to all the agility drivers. FLCADi

denotes the fuzzy level of change of the ith driver; FRLADACij denotes the fuzzy relationship-level between driver i andcapability j.

FARWAPk ¼Xn

j¼1

ðFRLACAPjk � FARWACjÞXn

j¼1

FARWACj

,; ð9Þ

where FARWAPk denotes the fuzzy average relation-weight of the kth provider to all the agility capabilities. FARWACj

denotes the fuzzy average relation-weight of the jth capability derived from Eq. (1); FRLACAPjk denotes the fuzzy rela-tion-level between capability j and provider k.By applying Eqs. (2a), (2b), (8) and (9), the fuzzy average relation-weights of the agility capabilities and providers canbe calculated. The results are listed in Table 5.

(5) Aggregate the fuzzy ratings and fuzzy average relation-weights of the agility capabilities into an FAI. The fuzzy-agilityindex (FAI) is an information measure, which consolidates fuzzy ratings and fuzzy average relation-weights of all agil-ity capabilities used to make appropriate responses to changes occurring in the supply chain business environment.The higher the FAI of a supply chain is, the higher its agility.According to the fuzzy weighted average operation [18,23], the FAI is defined as

FAI ¼Xn

j¼1

ðFMACj � FARWACJÞXm

i¼1

FARWACJ

,; ð10Þ

Table 5Average fuzzy relation-weights of agility capabilities and agility providers.

Agility capability Average fuzzy relation-weights

Agility providers Average fuzzy relation-weights

Sensing/identifying changes and responding(AC1).

(0.61,0.76,0.92) Multi-skilled and flexible personnel (AP1) (0.60,0.74,0.90)

Strategic vision (AC2). (0.60,0.76,0.91) Workforce skill upgrade (AP2) (0.55,0.72,0.88)Technological ability and appropriate product

introduction (AC)(0.65,0.79,0.93) Quick new product introduction (AP3) (0.60,0.76,0.91)

Cost-effectiveness (AC4) (0.61,0.77,0.92) Response to changing market requirements(AP4)

(0.63,0.78,0.93)

Cooperation and operations efficiency andeffectiveness (AC5)

(0.58,0.75,0.91) Products with substantial value-addition(AP5)

(0.52,0.70,0.87)

Product volume/model flexibility (AC6) (0.54,0.73,0.89) First-time right design (AP6) (0.62,0.77,0.93)Organization/personnel flexibility (AC7) (0.46,0.63,0.76) Trust-based relations with customers/

suppliers (AP7)(0.55,0.73,0.89)

Product/service design, delivery alacrity andtimeliness (AC8)

(0.67,0.82,0.96) Technology awareness (AP8) (0.54,0.72,0.88)

Fast operation time (AC9) (0.65,0.81,0.95) Skill and knowledge enhancement (AP9) (0.60,0.75,0.9)Concurrent execution of activities (AP10) (0.60,0.76,0.91)Information technology andcommunication (AP11)

(0.60,0.75,0.9)

Empowerment and decentralized decision-making (AP12)

(0.52,0.71,0.88)

Cross-functional team (AP13) (0.55,0.73,0.89)Culture of change (AP14) (0.37,0.58,0.78)


where FMACj denotes the fuzzy merit of the jth agility capability and FARWACj denotes the fuzzy average relation-weight of the jth capability derived from Eq. (8).By applying Eq. (10), the FAI for the EA supply chain was obtained as

FAI ¼ ð0:37;0:56;0:75Þ:

(6) Match FAI with an appropriate linguistic level. Once the FAI has been obtained, the committee further approximated alinguistic label whose meaning is the same as (or closest to) that of the FAI from the natural-language agility-level (AL)expression set.Several methods for matching the membership function with linguistic terms have been proposed. Three basic tech-niques include (1) Euclidean distance, (2) successive approximation, and (3) piecewise decomposition. The Euclideandistance method is most frequently utilized because it is the most intuitive form of human proximity perception [14].The Euclidean method consists of calculating the Euclidean distance from the given membership function to eachfunction representing the natural-language agility level expression set. Suppose that the natural-language agility levelexpression set is AL, UFAI and UALi are the membership functions of FAI and the natural-language agility level expres-sion, respectively. The distance between the fuzzy number FAI and each fuzzy-number ALi2 AL can then be calculatedas

dðFAI;ALiÞ ¼Xx2p

UFAIðxÞ � UALiðxÞ

� �2

( )1=2

ð11Þ

where p = {x0,x1, . . . ,xm} � [0,1] so that 0 = x0 < x1 < � � � < xm = 1.0. To simplify, let p = {0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1}. The distance from the FAI to each of the membersin the AL set can then be calculated and the closest natural expression with the minimum distance identified.In translating the FAI back into linguistic terms, one can choose the labels and membership functions depending onone’s experience and needs. In this case, the set AL = {extremely agile [EA], very agile [VA], highly agile [HA], agile[A], slightly agile [SA], non-agility [NA]} was selected for labeling, and the linguistics and corresponding membershipfunctions of this set are shown in Fig. 4. Using Eq. (11) the Euclidean distance D from the FAI to each member in set ALwas then calculated:

DðFAI; EAÞ ¼ 2:1509; DðFAI; VAÞ ¼ 1:6983; DðFAI;HAÞ ¼ 0:4014;

DðFAI;AÞ ¼ 1:4369; DðFAI; SAÞ ¼ 2:1266; DðFAI;NAÞ ¼ 2:1509:

Thus, by matching a linguistic label with the minimum D, the agility level of EA supply chain can be labeled as ‘‘highlyagile’’, as shown in Fig. 4.

1.0

f(x)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 x

FAI

NA SA A HA VA EA

Fig. 4. Linguistic levels to matching FAI. [NA (0,0.15,0.3); SA (0.15,0.3,0.45); A (0.3,0.45,0.6); HA (0.4,0.55,0.7); VA (0.55,0.7,0.85); EA (0.7,0.85,1.0)].

Table 6Comparison the results of FLQFDAEM and QFD relationship matrix approach.

Approach Agility index Range Linguistic translation

FLQFDAEM (0.37,0.56,0.75) 0.38 Highly agileQFD relationship matrix 0.56


(7) Comparison study. Because the FLQFDAEM is an extension of the QFD relationship matrix, to ascertain the efficiency ofthis method, a comparison study of the FLQFDAEM and the QFD relationship matrix approach was made by the eval-uation committee to ascertain the method’s efficiency.The ambiguity and multiplicity within factors are ignored when using the QFD relationship matrix approach in agilityevaluation. The evaluators were asked to use a scale to score the criteria directly or to use linguistic terms to assess thecriteria. The linguistic terms were translated into a crisp scale for computing the agility index of the firm. In the com-parison study, the team of experts used the ‘‘core’’ fuzzy number members to represent linguistic values in the QFDrelationship matrix approach. For example, the triangular fuzzy number (0.5,0.65,0.8) was used to approximate thelinguistic variable ‘‘Good’’; therefore the core member 0.65 was adopted to represent the linguistic variable ‘‘Good’’in the QFD relationship matrix approach.The results were compared with those derived from the FLQFDAEM listed in Table 6. From the agility index scale pointof view, the results generated by both approaches appear to lead to similar conclusions. However, the FAI generated bythe FLQFDAEM approach was expressed in terms of a range of values. This index can provide an overall picture of therelevant probability and ensure that the decision made in the subsequent evaluation process is not biased. Further, itallows the managers a higher degree of flexibility in decision-making. As an example, an agility index having a fuzzyvalue (0.37,0.56,0.75) indicates that the agility level is closer to ‘‘highly agile,’’ but also not far from ‘‘Very agile.’’ How-ever, a crisp index of 0.56 generated by the relationship matrix in QFD approach may have different implications orprovide less rich information. This comparison demonstrates that the FLQFDAEM approach can provide the analystwith more informative and reliable results.

(8) Analysis and suggestions. As mentioned in the previous section, an agility evaluation determines the agility of anenterprise and also, most importantly, helps managers identify the principal adverse factors for implementing anappropriate plan to enhance the agility level.

Agility providers are supposed to provide and determine the entire agile behavior set of an enterprise. To identify theprincipal obstacles to enhancing the agility level, a fuzzy agility-provider merit-relation-value index (FAPMRVI) combiningthe merit ratings and the FARWAP derived from Eq. (9), represents an effect that will contribute to the agility of a supplychain. The lower the FAPMRVI of a factor is, the lower the degree of contribution for the factor. Thus, the FAPMRVI scoreof a factor is used to identify the principal adverse factors.

If the FARWAP is used to calculate FAPMRVI directly, the importance of FARWAP will neutralize the merit ratings; there-fore, the actual principal obstacles (low merit rating and high average relation-weight) cannot be identified. If a high value isgiven to FARWAPk, then [(1,1,1)hFARWAPk] becomes a low value. Hence, to elicit the factor with the lowest merit rating andthe highest average-relation-weight for each agility provider k, the fuzzy index for FAPMRVIk is defined as

FAPMRVIk ¼ FMAPk � FARVAP0k; ð12Þ

where FARVAP0k ¼ ½ð1;1;1ÞhFARWAPk�; FMAPk denotes the fuzzy merit of the kth agility provider.Since the EA supply chain agility index is ‘‘highly agile’’ (according to the evaluation), far from the ‘‘extremely agile’’

objective, obstacles within the organization impact or stop the company becoming ‘‘extremely agile’’. By applying Eq.(12), the fourteen fuzzy agility-provider merit-relation-value indexes (FAPMRVIs) listed in Table 7 are obtained.

Table 7Fuzzy merit-relation-value indexes of agility providers.

Agility providers Merits of agility provider (1.0,1.0,1.0) (�) FARWAPi Fuzzy relation-value indexes Ranking scores

AP1 (0.3,0.5,0.7) (0.1,0.26,0.4) (0.03,0.13,0.28) 0.1808AP2 (0.5,0.65,0.8) (0.12,0.28,0.45) (0.06,0.182,0.36) 0.2339AP3 (0.7,0.8,0.9) (0.09,0.24,0.4) (0.063,0.192,036) 0.2391AP4 (0.5,0.65,0.8) (0.07,0.22,0.37) (0.035,0.143,0.296) 0.1929AP5 (0.5,0.65,0.8) (0.13,0.3,0.48) (0.065,0.195,0.384) 0.2478AP6 (0.3,0.5,0.7) (0.07,0.23,0.38) (0.021,0.115,0.266) 0.1681AP7 (0.5,0.65,0.8) (0.11,0.27,0.45) (0.055,0.176,0.36) 0.2305AP8 (0.7,0.8,0.9) (0.12,0.28,0.46) (0.084,0.224,0.414) 0.2722AP9 (0.5,0.65,0.8) (0.1,0.25,0.4) (0.05,0.163,0.32) 0.2115AP10 (0.5,0.65,0.8) (0.09,0.24,0.4) (0.045,0.156,0.32) 0.2077AP11 (0.5,0.65,0.8) (0.1,0.25,0.4) (0.05,0.163,0.32) 0.2115AP12 (0.5,0.65,0.8) (0.12,0.29,0.48) (0.06,0.189,0.384) 0.2444AP13 (0.3,0.5,0.7) (0.11,0.27,0.45) (0.033,0.135,0.315) 0.1947AP14 (0.3,0.5,0.7) (0.22,0.42,0.63) (0.066,0.21,0.441) 0.2709


By applying Eqs. (3)–(7), the FAPMRVIs were defuzzified, as listed in Table 7. These indices represent the effect of eachprovider contributing to the EA supply chain agility level. Based on the Pareto principle, the committee decided to focus theirresources on a few critical factors and set a scale of 0.2 as the management’s threshold for identifying the factors forimprovement. As shown in Table 7, four providers performed lower than the threshold, namely (1) first-time right design,(2) multi-skilled and flexible personnel, (3) response to changing market requirements, and (4) cross-functional teams. Theseproviders represent the most significant contributions for enhancing supply chain agility. In connection with the weakestproviders within the organization, the committee suggested that a corrective action plan be implemented to improve theadverse providers and enhance the supply chain agility level.

After five years and ten cycles of continuous implemented improvement, the EA supply chain agility index has risen toapproach the ‘‘extremely agile’’ level. The managers are able to immediately capture information on demand from all overthe world to make rapid and appropriate decisions to respond more efficiently and effectively to customers. The tangiblebenefits are reducing the mean lead-time for responding to customers’ demands by approximately 36.7% under the sameinventory level; average sales increased by 11%, 23%, 27%, 17% and 19% during the past five years; an ascent from ninthto fourth position in the world market, especially boosted by becoming the leading brand of PCs and notebooks in the Euro-pean market.

7. Discussion and conclusions

The agility of an enterprise is perceived as the dominant competitive vehicle. This report highlighted the following ques-tions: What is agility? How should the appropriate agile enablers be adopted to develop enterprise agility? How close is theenterprise to becoming agile? How can the enterprise effectively improve its agility? For an enterprise to achieve agility, it iscritical to aligning and integrate agility providers, capabilities and drivers to ensure that the agility providers can satisfy theagility capabilities and the agility capabilities can cope with agility drivers, transforming them into strategic competitiveedges. Although important concepts and steps for achieving agility have been identified, there is still no systematic toolfor integrating and dealing with issues of the interface among these steps. Most of the existing approaches for agility devel-opment are structural in nature. Conventional (crisp) evaluation approaches, which are unsuitable and ineffective for han-dling situations, which by nature lead to complexity and vagueness, have been evaluated. To compensate for theselimitations, a QFD-based framework to logically link up and deal with issues of the interface and coordination among theagility provider, capability and driver was proposed. The proposed methodology provides a new systematic structure fortranslating the agility drivers in the business environment into the capabilities that are needed and subsequently for deter-mining the requirements for action (agility-enabled attributes). In addition, a fuzzy agility index (FAI) composed of agilitycapability ratings and its relation-weights with drivers was developed for the measurement of agility in an enterprise. Thisreport also described how the proposed approach was applied to develop agility in a Taiwanese PC enterprise. Throughdevelopment and evaluation, it has been shown that the proposed framework and procedures can enhance the agility ofan enterprise, as well as ensure a competitive edge.

The proposed method was developed from the QFD relationship matrix concept and adapted for an information technol-ogy (IT) enterprise which served as an initial case study for validating the model and approach. The enterprise and managersinvolved in the case study were generally pleased with the approach. This work provides potential value to practitioners byoffering a rational structure to logically integrate different elements at various stages of strategic planning. The uncertaintyand vagueness of assessment for each attribute and relationship were addressed to ensure relatively realistic information.The unprecedented application of the QFD and fuzzy logic was demonstrated to researchers.

Although this case study has demonstrated the usefulness of the proposed approach for enterprise agility developmentplanning, it may be very valuable for a company to use the FLQFDAEM approach and other methods such as the AHP, TOPSIS,


and weighted sum method because each one uses different theoretical approaches and algorithms for enterprise agility eval-uation. Furthermore, believing that there are areas for future validation and improvement, it hopes to encourage additionalmanagers to adopt our method. A single case study or a number of case studies does not necessarily provide a true measureof the relative performance and success of this model. Further research should be performed to bring this method tomaturity and compare the method’s efficiency in different types of planning (such as information-strategy, marketing,product-roadmap, knowledge-management). This approach does not focus on finding an optimal deployment method butmerely addresses prioritizing agility providers. For further research, a goal-programming model can be developed to selectin greater detail the combination of agility capabilities and providers that results in optimal levels of agility, when subject tocost and other enterprise constraints.

The evaluation levels and members involved in any particular implementation will be different, depending on the firminvolved. The agility drivers and entrepreneurial objectives and strategies vary from firm to firm. For example, enterprisesin high-tech industries, stressing competitive advantage through innovation, may decide on agility capabilities and providersdifferently from firms in traditional industries seeking to compete with flexibility, global sourcing and low-cost providers. Inaddition, a model cannot consider all agility factors. Different enterprises with dissimilar characteristics and circumstancesexperience various agility drivers that are specific and perhaps unique to them. It must be emphasized that the eleven criticalagility drivers, nine vital agility capabilities and fourteen fundamental agility providers presented in this work are by nomeans exhaustive. Therefore, new factors may be added/amended, depending on the product, industry, market and enter-prise goals.

According to the comments from the previous case, this approach resolves some of the problems in traditional strategicbusiness planning methods, having several advantages compared to previous methods:

(1) This method provides a structured procedure for agility operational strategy planning, not only agility measurement.The FLQFDAEM procedure starts by identifying the agility drivers in a business environment, thereby deploying capa-bilities needed to finally determine the providers. Thus, the market drivers and company objectives can be efficientlyand effectively connected and translated into choosing agility providers and enhancing enterprise agility. The casestudy demonstrated that having providers aligned with strategy and drivers ensures that the providers can satisfyand cope with the strategic direction and provide a competitive edge for the enterprise.

(2) This method gives the analyst more convincing, informative and reliable results. The FAI was expressed as a range ofvalues, providing an overall description of the agility of an enterprise and ensuring that the decision made in the eval-uation is not biased as well as being requisite for obtaining organization managers buy-in.

(3) This method provides diagnosis and coordinates inter-functional conflicts, enhancing understanding by the differentfunctional units as well as the implementation of organization-wide strategy planning.

(4) This method provides a guiding, dynamic document linking the business strategy of a firm with its environment andoutlines the details for implementation through the continuous improvement of processes and total qualitymanagement.

(5) This method provides a first step in preventing a majority of inappropriate assessments and also expedites the even-tual financial analysis by highlighting the most important benefits and drawbacks for formulating a comprehensiveplan for improvement.

Although it can be concluded that this new method is feasible and efficient for defining, aligning, measuring, and improv-ing agility in an agility development project, there are some limitations to the fuzzy-logic approach. The natural languageexpression membership function depends on the managerial perspective of the experts, who must be at a strategic levelin the enterprise to evaluate the importance of all aspects such as strategy, marketing and technology. Furthermore, com-petitive situations and requirements vary from one enterprise or industry to another. Hence, a company must establish un-ique membership functions appropriate to its own specific environment and considerations. The computation of a fuzzyweighted average is still complicated and not easily appreciated by managers. Fortunately, this calculation has been com-puterized to increase accuracy while reducing both computation time and the possibility of errors.

References

[1] A. Agarwal, R. Shankar, M.K. Tiwari, Modeling agility of supply chain, Industrial Marketing Management 36 (2007) 443–457.[2] A. Agarwal, R. Shankar, M.K. Tiwari, Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach, European Journal of

Operational Research 73 (2006) 211–225.[3] S. Aggarwal, Flexibility management: The ultimate strategy, Industrial Management 39 (1) (1997) 26–31.[4] E. Bottani, A fuzzy QFD approach to achieve agility, International Journal of Production Economics 119 (2009) 380–391.[5] V. Bouchereau, H. Rowlands, Quality function deployment: The unused tool, Engineering Management Journal February (2000) 45–52.[6] O. Cao, M.J. Schniederjans, Empirical study of the relationship between operations strategy and information systems strategic orientation in an

e-commerce environment, International Journal of Production Research 42 (15) (2004) 2915–2939.[7] T.H. Chang, T.C. Wang, Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management,

Information Sciences 179 (2009) 355–370.[8] S.J. Chen, C.L. Hwang, Fuzzy Multiple Attribute Decision Making Methods and Applications, Springer-Verlag, Berlin Heidelberg, 1992.[9] M. Chuang, Y.S. Yang, C.T. Lin, Production technology selection: Deploying market requirements, competitive and operational strategies, and

manufacturing attributes, International Journal of Computer Integrated Manufacturing 22 (4) (2009) 345–355.[10] D. Dubois, H. Prade, Possibility Theory – An Approach to Computerized Processing of Uncertainty, Plenum, New York, 1988.


[11] A. Ganguly, R. Nilchiani, J.V. Farr, Evaluating agility in corporate enterprises, International Journal of Production Economics 118 (2009) 410–423.[12] S. Ghazinoory, A. Esmail Zadeh, A.S. Kheirkhah, Application of fuzzy calculations for improving portfolio matrices, Information Sciences 180 (2010)

1582–1590.[13] S.L. Goldman, R.N. Nagel, K. Preiss, Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer, Van Nostrand, Reinhold, New

York, 1995.[14] H.W. Guesgen, J. Albrecht, Imprecise reasoning in geographic information systems, Fuzzy Sets and Systems 113 (2000) 121–131.[15] A. Gunasekaran, Agile manufacturing: Enables and an implementation framework, International Journal of Production Research 36 (5) (1998)

1223–1247.[16] T.H. Hsu, T.N. Tsai, P.L. Chiang, Selection of the optimum promotion mix by integrating a fuzzy linguistic decision model with genetic algorithms,

Information Sciences 179 (2009) 41–52.[17] H. Ismail, I. Reid, J. Mooney, J. Poolton, I. Arokiam, How small and medium enterprises effectively participate in the mass customization game, IEEE

Transactions on Engineering Management 54 (1) (2007) 86–97.[18] M. Jackson, C. Johansson, An agility analysis from a production system perspective, Integrated Manufacturing Systems 14 (6) (2003) 482–488.[19] V. Jain, L. Benyoucef, S.G. Deshmukh, A new approach for evaluating agility in supply chains using fuzzy association rules mining, Engineering

Applications of Artificial Intelligence 21 (2008) 367–385.[20] S.M.R. James-Moore, Agility is easy, but effective agile manufacturing is not, in: The Institution of Electrical Engineers, IEE, London, 1997.[21] C. Kao, S.T. Liu, Fractional programming approach to fuzzy weighted average, Fuzzy Sets and Systems 120 (2001) 435–444.[22] W. Karwowski, A. Mital, Applications of approximate reasoning in risk analysis, in: W. Karwowski, A. Mital (Eds.), Applications of Fuzzy Set Theory in

Human Factors, Netherlands, Amsterdam, 1986.[23] P.T. Kidd, Agile Manufacturing: Forging New Frontiers, Addison-Wesley, Reading, MA, 1994.[24] S.H. Kim, D.H. Jang, D.H. Lee, S.H. Cho, A methodology of constructing a decision path for IT investment, Journal of Strategic Information Systems 9

(2000) 17–38.[25] A. Kumar, J. Motwani, A methodology for assessing time-based competitive advantage of manufacturing firms, International Journal of Operations and

Production Management 15 (2) (1995) 36–53.[26] D.H. Lee, D. Park, An efficient algorithm for fuzzy weighted average, Fuzzy Sets and Systems 87 (1997) 39–45.[27] K.H. Lee, J.H. Lee, A method for ranking fuzzy numbers and its application to decision-making, IEEE Transactions on Fuzzy Systems 7 (6) (1999)

677–685.[28] S.H. Liao, S.H. Ho, Investment project valuation based on a fuzzy binomial approach, Information Sciences 180 (2010) 2124–2133.[29] C.T. Lin, C.T. Chen, A fuzzy-logic-based approach for new product Go/No-Go evaluation at the front end, IEEE Transactions on Systems, Man, and

Cybernetics. Part A: Systems and Humans 34 (1) (2004) 132–142.[30] L.L. Machacha, P. Bhattacharya, A fuzzy logic-based approach to project selection, IEEE Transactions on Engineering Management 47 (1) (2000) 65–73.[31] L.M. Meade, K.J. Rogers, Enhancing a manufacturing business process for agility, in: The Portland International Conference on Management and

Technology, Portland, 1997.[32] G.A. Miller, The magic number seven, plus or minus seven, Psychological Review 63 (1965) 81–97.[33] J.G. Miller, W. Hayslip, Implementing manufacturing strategic planning, Planning Review 17 (4) (1989) 23–48.[34] H. Moskowitz, K.J. Kim, QFD optimizer: A novice friendly quality function deployment decision support system for optimizing product design,

Computer and Industrial Engineering 32 (1997) 641–655.[35] NGM Project Office, NGM: Next generation manufacturing, a framework for action, in: K. Kosanke, J.G. Nell (Eds.), Enterprise Engineering and

Integration: Building International Consensus, Springer-Verlag, Berlin, 1997.[36] W. Pedrycz, Why triangular membership functions?, Fuzzy Sets and Systems 64 (1994) 21–30[37] M.E. Porter, Competitive Advantage: Creating and Sustaining Superior Performance, The Free Press, New York, 1985.[38] K. Preiss, S.L. Goldman, R.N. Nagel, Cooperate to Compete: Building Agile Business Relations, Van Nostrand, Reinhold, New York, 1996.[39] J. Ren, Y.Y. Yusuf, N.D. Nagel, A prototype of a measurement system for an agile enterprise, in: The Third International Conference of Quality Reliability

Maintenance, Oxford, UK, 2000.[40] R.A. Roussel, K.N. Saad, J.J. Erickson, Third Generation R&D: Managing the Link to Corporate Strategy, Harvard Business Press, Boston, MA, 1991.[41] H. Sharifi, Z. Zhang, Methodology for achieving agility in manufacturing organization: An introduction, International Journal of Production Economics

62 (1999) 7–22.[42] H. Sharifi, Z. Zhang, Agile manufacturing in practice: Application of a methodology, International Journal of Operations and Production Management 21

(5–6) (2001) 772–794.[43] J.M. Sharp, Z. Irani, S. Desai, Working towards agile manufacturing in the UK industry, International Journal of Production Economics 62 (1999)

155–169.[44] B. Sherehiy, W. Karwowski, J.K. Layer, A review of enterprise agility: Concepts, frameworks, and attributes, International Journal of Industrial

Ergonomics 37 (2007) 445–460.[45] P.M. Swafford, S. Ghosh, N. Murthy, The antecedents of supply chain agility of a firm: Scale development and model testing, Journal of Operations

Management 24 (2006) 170–188.[46] N.C. Tsourveloudis, K.P. Valavanis, On the measurement of enterprise agility, International Journal of Intelligent and Robotic systems 33 (3) (2002)

329–342.[47] W.P. Wang, Toward developing agility evaluation of mass customization systems using 2-tuple linguistic computing, Expert Systems with Applications

36 (2) (2009) 3439–3447.[48] M.M. Weber, Measuring supply chain agility in the virtual organization, International Journal of Physical Distribution and Logistical Management 32

(7) (2002) 577–590.[49] S.C. Wheelwright, R.H. Hayes, Competing through manufacturing, Harvard Business Review (1985) 99–109.[50] S.L. Yang, T.F. Li, Agility evaluation of mass customization product manufacturing, Journal of Materials Processing Technology 129 (2002) 640–644.[51] J. Yen, R. Langari, Fuzzy Logic: Intelligence, Control and Information, Prentice-hall, Upper Saddle River, NJ, 1999.[52] M.A. Youssuf, The impact of the intensity level of computer-based technologies on quality, International Journal of Operations and Production

Management 14 (4) (1993) 4–25.[53] Y.Y. Yusuf, A. Gunasekaran, E.O. Adeleye, K. Sivayoganathan, Agile supply chain capabilities: Determinants of competitive objectives, European Journal

of Operational Research 159 (2004) 379–392.[54] Y.Y. Yusuf, M. Sarhadi, A. Gunasekaran, Agile manufacturing: The drivers, concepts and attributes, International Journal of Production Economics 62

(1999) 33–43.[55] Y.Y. Yusuf, J. Ren, N.D. Burns, A method for evaluating enterprise agility – An empirical study, in: 16th International Conference on Production

Research, Prague, Czech Republic, 2001.[56] Z. Zhang, H. Sharifi, Towards theory building in agile manufacturing strategy – A taxonomical approach, IEEE Transactions on Engineering

Management 54 (2) (2007).[57] Z. Zhang, H. Sharifi, A methodology for achieving agility in manufacturing organizations, International Journal of operations and Production

Management 20 (4) (2000) 496–512.

enhancing enterprise agility by deploying agile drivers, capabilities and providers

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