InTech uses cookies to offer you the best online experience. By continuing to use our site, you agree to our Privacy Policy.

Engineering » Industrial Engineering and Management » "Supply Chain", book edited by Vedran Kordic, ISBN 978-3-902613-22-6, Published: February 1, 2008 under CC BY-NC-SA 3.0 license. © The Author(s).

Chapter 12

Align Agile Drivers, Capabilities and Providers to Achieve Agility: a Fuzzy-Logic QFD Approach

By Chwei-Shyong Tsai, Chien-Wen Chen and Ching-Torng Lin
DOI: 10.5772/5344

Article top

Align Agile Drivers, Capabilities and Providers to Achieve Agility: a Fuzzy-Logic QFD Approach

Chwei-Shyong Tsai1, Chien-Wen Chen2 and Ching-Torng Lin3

1. Introduction

At the beginning of the twenty-first century, the world faces profound changes in many aspects, especially marketing competition, technological innovations and customer demands. A world-wide dispersion of education and technology has led to intense and increasingly global competition and an accelerated rate of change in the marketplace and innovation. There is a continuing fragmentation of mass markets into niche markets, as customers become more demanding with their increasing expectations. This critical situation has led to major revisions in business priorities, strategic vision, and the viability of conventional and even relatively contemporary models and methods developed thus far [1]. To cope with these changing competitive markets, as well as the ability to meet customer demands for increasingly shorter delivery times, and to ensure that the supply can be synchronized to meet the peaks and troughs of the demand are obviously of critical importance [2, 3]. Hence, companies now require a high level of maneuverability encompassing the entire spectrum of activities within an organization. Consequently, agility in addressing new ways to manage enterprises for quick and effective reaction to changing markets, driven by customer-designed products and services, has become the dominant vehicle for competition [4].

Generally, agility benefits can mass customization, increase market share, satisfy customer requirements, facilitate rapid introduction of new products, eliminate non-value-added activities, reduce product costs and increase the competitiveness of enterprises. Accordingly, agility has been advocated as the business paradigm of the 21st century, being considered the winning strategy for becoming a global leader in an increasingly competitive market of quickly changing customer requirements [5-7]. However, the ability to build agility has not developed as rapidly as anticipated, because the development of technology to manage an agile enterprise is still in progress [4, 6, 8]. Thus, in embracing agility, many important questions must be asked, such as: Precisely what is agility, and how can it be measured? How will companies know when they possess this attribute since no simple metrics or indices are available? How and to what degree do the attributes of an enterprise affect its business performance? How does one compare agility with a competitive enterprise? To improve entrepreneurial agility, how does one identify the principal unfavorable factors? How can one assist in more effectively achieving agility [8-10]? Answers to such questions are critical to practitioners and the theory of agile entrepreneurial design. Therefore, the purpose of this research is to seek solutions to some of these problems, with a particular focus on agile strategic planning and measurement, as well as identifying the principal obstacles to improvement of agility.

Actually, the purpose of agile strategic planning is to unite the resources of an enterprise and to create business value. Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment and making an appropriate response; therefore, these enterprises require a number of distinguishing attributes to promptly deal with the changes within their environment. Such attributes consist of four principal elements [7, 8]: responsiveness, competency, flexibility/adaptability and quickness/speed. Furthermore, the foundation for agility is comprised of the integration of information technologies, personnel, business process organization, innovation and facilities into strategic competitive attributes. To be truly agile, an enterprise must logically integrate and deploy a number of distinguishing providers with drivers and good capabilities, being finally transformed into strategic competitive edges [11].

Many theoretical models have been proposed for agile enterprise planning [1, 12-15]; however, only a few provide integrated methodologies suitable for adoption to enhance by identifying providers, beginning with the competitive bases of the enterprise. The relationship matrix in the quality function deployment (QFD) method provides an excellent tool for aligning important concepts and linking processes. Moreover, fuzzy logic is a useful tool for capturing the ambiguity and multiplicity of meanings of the linguistic judgments required to express both relationships and rates of agility attributes.To assist managers in more efficiently achieving agility, a systematic methodology, based on fuzzy logic and the relationship matrix in the QFD is devised to provide a means for linking the perspectives from agility drivers with their corresponding capabilities and providers, thereby measuring the agility of an enterprise as well as identifying the principal obstacles to improvement.

The remainder of this report is organized as follows. In Section II the related research is reviewed. In section III a conceptual model of an agile enterprise is described in detail for the development of a systematic evaluative methodology in Section IV. The development of a practical case is presented illustrated in Section V. Finally, Section VI a concluding discussion.

2. Review of related research

A. Methodology

Numerous studies for developing methodologies have been proposed to assist managers in the implementation of strategic planning for achieving agility. For example, to promote a new understanding of cooperation as a vital means of survival and prosperity in the new business era, Preiss et al. [12] proffered a generic model for approaching agility. This model consists of certain steps that can assist an enterprise in understanding its business environment and the changes occurring there, the attributes enabling the infrastructure, and the business processes that should be recognized in the subsequent actions of the organization to sustain its competitive advantage. The first integrated framework to achieve agility was proposed by Gunasekaran [15]. The framework explains how the major capabilities of agile manufacturing should be supported and integrated with appropriate providers to develop an adaptable organization. Seeking to exploit the concept and practices of agility, two research teams [1, 10] have developed a three-step methodology for achieving agility. This methodology provides manufacturing companies with a tool for understanding the total concept of agility, assessing their current positions, determining their need for agility and the capabilities required for achievement, as well as adopting relevant practices which can induce these capabilities. A three-step model was also suggested by Jackson and Johansson [14] to analyze the agility of production systems. Their methodology begins with an assessment of the degree of market turbulence, to determine the relevance of agility in a specific context. Then, the strategic view of the company is examined, with a particular focus on potentials to enhance flexibility and change competencies as viable strategies to achieve a competitive advantage.

Although structured frameworks to formulate agility have been identified, most of them for strategic formulation are structural in nature. Thus, to assure that the providers can satisfy the strategic direction of an enterprise, an integrated methodology suitable for adoption to enhance agility by identifying its providers, beginning with competitive bases of the enterprise, is critical to both practitioners and the theory of agile enterprise design.

B. Measurement

Many approaches to the measurement of agility have been proposed to assist managers in assessment; however, most of these methods assess only the capabilities of agility. Some authors [10, 16, 17] have defined an agility index as a combination of measurement of the intensity levels of enabling attributes; whereas, other measuring methods [18,19] have been developed on the basis of the logical concept of an analytical hierarchical process (AHP). An evaluation index for a mass-customization product manufacturing agility was devised by Yang and Li [20]. Furthermore, to overcome the vagueness of agility assessment, Tsourveloudis and Valavanis [21] designed some IF-THEN rules based on fuzzy logic; moreover, Lin et al. [6] developed a fuzzy agility index (FAI) based on providers using fuzzy logic. Each of these techniques, however, with the exception of the agility providers, seems to address only a limited aspect of a very complicated problem. Although each technique contributes to an understanding of the problem, each - functioning alone - is insufficient for handling the problem in its entirety because the selection of the provider and the assessment should be linked with the drivers and the capabilities [22]. It is therefore necessary to examine the problem from a broader perspective.

C. QFD Relationship Matrix

The QFD method was designed to emphasize detailed pre-planning to meet customer needs and requirements for new product development. It employs several charts, called house of quality (HOQ), to translate the desires of the customer into the design or engineering characteristics of the product and subsequently into the characteristics of the parts, process plan and production requirements related to its manufacture. Phase I translates the voice of the customer into corresponding engineering characteristics; phase II moves one step backward in the design process by translating the engineering characteristics into characteristics of the parts; phase III identifies the critical process parameters and operations; and finally, phase IV identifies the detailed production requirements. The basic format of the HOQ consists of seven different major components: (1) customer requirements (CRs), (2) importance of customers’ requirements, (3) design requirements (DRs), (4) relationship matrix for CRs and DRs, (5) correlation among DRs (6) competitive analysis of competitors, and (7) prioritization of design requirements, as shown in Figure 1.

media/image1.jpeg

Figure 1.

A basic house-of-quality (HOQ) matrix.

Although QFD has been proposed for customer-driven product development and delivery methodology, an enterprise can achieve various corporate strategic goals such as a reduction in customer complaints, improvement in design reliability and customer satisfaction, easier design change, a reduction in product-development-cycle time, and organizational efficiency by using this method [23, 24]. Similarly, QFD can be extended for aligning drivers with providers to achieve agility and make priority decisions concerning the specific provider improvements that should be made for enhancing the agility level of an enterprise.

A simplified form of the HOQ matrix, in which the importance of customers’ requirements, correlation analyses among DRs are removed, is utilized in this study. This simplified form is called a relationship matrix, wherein CRs are represented on the left side. Identifying the relative importance of the various CRs is an important step in discerning those that are critical and also helps in prioritizing the design effort. DRs are represented on the upper portion of the relationship matrix. The relative importance of the DRs can be calculated by using the relative importance of the CRs and the level assigned to the relationships between CRs and DRs, presented in the main body of the matrix, which can be represented in symbolic or numerical form. The level of the relationships is typically assessed by an evaluation team in a subjective manner.

D. Fuzzy Logic

A fuzzy set can be defined mathematically by assigning a value to each possible member in a universe representing its grade of membership. Membership in the fuzzy set, to a greater or lesser degree, is indicated by a larger or smaller membership grade. Fuzzy-set methods allow uncertain and imprecise systems of the real world to be captured through the use of linguistic terms so that computers can emulate human thought processes. Thus, fuzzy logic is a very powerful tool capable of dealing with decisions involving complex, ambiguous and vague phenomena that can be assessed only by linguistic values rather than by numerical terms. Fuzzy logic enables one to effectively and efficiently quantify imprecise information, perform reasoning processes and make decisions based on vague and incomplete data [25]. On the basis of previous study [26], the experts can make a significant measurement of the possibility of an event when it is known; however, in uncertain situations characterized by either a lack of evidence or the inability of the experts to make a significant measurement when available information is scarce, managers often react very incompetently. Fuzzy logic, by making no global assumptions about the independence, exhaustiveness, or exclusiveness of the underlying evidence, tolerates a blurred boundary in definitions [25]. Thus, fuzzy logic brings the hope of incorporating qualitative factors into decision-making.

Fuzzy logic is currently being used extensively in many industrial applications as well as in managerial decision making. For example, it has been used in multi-attribute decision-making situations to select R&D project evaluation [27]. Ben Ghalia et al. [28] used fuzzy-logic inference for estimating hotel-room demand by eliciting knowledge from hotel managers and building fuzzy IF-THEN rules. 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 competitive advantages, superiority, technological suitability and risk. Chen and Chiou [30] devised a fuzzy credit rating for commercial loans. Hui et al. [31] obtained data from experienced supervisors to create a fuzzy-rule-based system for balance control of assembly lines in apparel manufacturing. Organizational transformations have been widely adopted by firms to improve competitive advantage. Chu et al. [32] uses a nonadditive fuzzy integral to develop a framework to assess performance of organization transformation.

3. Conceptual model of agile enterprise

The goal of an agile enterprise is to enrich/satisfy customers and employees. An enterprise essentially possesses a set of capabilities for making appropriate responses to changes occurring in its business environment. However, the business conditions in which many companies find themselves are characterized by volatile and unpredictable demand; thus, there is an increasing urgency for pursuing agility. Agility might, therefore, be defined as the capability of an enterprise to respond rapidly to changes in the market and customers’ demands. To be truly agile, an enterprise should possess a number of distinguishing agility-providers. From a review of the relevant literature [1, 4, 6, 12, 14], the author has developed a conceptual model of an agile enterprise, as shown in Figure 2.

media/image2.jpeg

Figure 2.

A conceptual framework of an agile enterprise.

The main driving force behind agility is change. There is nothing new about change; however, change is currently occurring at a much faster rate than ever before. Turbulence and uncertainty in the business environment have become the main causes of failures in enterprises. The number of changes and their type, specification or characteristics cannot be easily determined and probably is indefinite. Different enterprises with dissimilar characteristics and circumstances experience various changes that are specific and perhaps unique to themselves. However, there are some common characteristics in changes that occur, which can produce a general consequence for all enterprises. By summarizing previous studies [1, 4, 7, 8], the general areas of change in a business environment can be categorized as (1) market volatility caused by growth of the market niche, increasing introduction of new product and shrinkage of product life; (2) intense competition caused by rapidly changing markets, pressure from increasing costs, international competitiveness, Internet usage and a short development time for new products; (3) changes in customer requirements caused by demands for customization, increased expectations for quality and quicker delivery time; (4) accelerating technological changes caused by the introduction of new and efficient production facilities and system integration; and (5) changes in social factors caused by environmental protection, workforce/workplace expectations and legal pressure.

Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment and 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 [7, 8]: (1) responsiveness, the ability to see/identify changes, to respond quickly, reactively or proactively, and to recover; (2) competency, the efficiency and effectiveness of an enterprise in reaching its goals; (3) flexibility/adaptability, the ability to implement different processes and achieve different goals with the same facilities; and (4) quickness/speed, the ability to culminate an activity in the shortest possible time.

Achieving agility requires responsiveness in strategies, technologies, personnel, business processes and facilities. Agility-providers should exhibit agile characteristics as well as make available and determine the agility capabilities and behavior of an enterprise. Numerous studies dedicated to identifying agility-providers from which organization leaders can select items appropriate to their own strategies, organizational business processes and information systems have been conducted. For example, Kumar and Motwani [33] identified twenty-three factors that influence a firm’s agility. Goldman et al. [34] suggested that agility has four underlying components: (1) delivering value to customers, (2) being ready for change, (3) valuing human knowledge and skills, and (4) forming virtual partnerships. The “next generation manufacturing” project identified six attributes for agility: (1) customers, (2) physical plant and equipment, (3) human resources, (4) global markets, (5) core competency, and (6) practices and cultures [35]. Moreover, Yusuf et al. [36] proffered a set of thirty-two agile attributes grouped into four dimensions: (1) core competency management, (2) virtual enterprise, (3) capability for reconfiguration, and (4) knowledge-driven enterprises. These attributes, representing most aspects of agility, determine the entire behavior of an enterprise. Most recently, Ren et al. [37], following the work of Yusuf et al. [36] based on a survey circulated among UK enterprises, conducted principal component analysis to confirm the correlations between the thirty-two attributes. Finally, six principal components encompassing fifteen attributes were identified as critical agility-enabling-attributes: (1) human knowledge and skills, (2) customization, (3) partnership and change, (4) technology, (5) integration and competence, and (6) team-building. From this review we can see that different researchers provide certain insights into different aspects of agility providers. It is highly probable that there is no single set of agility providers reflecting all aspects.

Although several researchers [1, 12-15] have accepted a conceptual model for achieve agility, the purpose of agile strategic planning is to unite the resources of an enterprise to compete with the change in environment and to create business value, which according to some studies [4, 22] can be maximized and the competitive threat minimized only by selecting agile providers for investments aligned to the company's business strategy and competitive bases in the market. Thus, the first priority should be to understand the relationships among the specific market field requirement, as well as the agility capabilities and providers, to deploy and integrate both capabilities and providers, and to transform them into a competitive edge.

To assist managers in more efficiently achieving agility, on the basis of the conceptual model of an agile enterprise, and by using the relationship matrix in the QFD approach, a systematic model for linking and integrating agility drivers, capabilities and providers, can be constructed as shown in Figure 3.

media/image3.jpeg

Figure 3.

A systematic agility-linking model.

Specifically, this model can be described as follows:

  • Analysis of agile strategy: to identify the degree of the agile abilities that can provide the required strength for responding to changes and searching for competitive advantage by maintaining alignment between agility drivers and agile abilities.

  • Identification of agile providers: to find agility providers constituting the means by which the so-called needs of an enterprise relation to capabilities can be achieved by linking between abilities and providers.

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

As mentioned in the previous section, the deployment and integration of agility drivers, capabilities and providers, and their transformation into a competitive edge is critical for achieving agility. Due to an either “imprecise” or “vague” definition of agile attributes and relationships, the deploying and integrating evaluation process is associated with uncertainty and complexity. Managers must make a decision by considering agile attributes and relationships which might have non-numerical values. All attributes must be integrated within the evaluation decision although none of them may exactly satisfy the ideals of the enterprises. Conventional "crisp" evaluation approaches cannot handle such decisions suitably or effectively. Since humans have the capability of understanding and analyzing obscure or imprecise events which are not easily incorporated into existing analytical methods, the corporate strategic planning decision is made primarily on the basis of the opinions of experts. On the basis of previous research [38], in situations where evaluators are unable to make a significant assessment, linguistic expressions are used to estimate ambiguous events. Linguistic terms usually have vague meanings. One way to capture the meanings of linguistic terms is to use the fuzzy-logic approach to associate each term with a possibility distribution [39].

To assist managers in more efficiently achieving agility by using the relationship matrix in the QFD approach and fuzzy logic, an evaluation algorithm composed of four major parts (as shown in Figure 4) was devised for development and evaluation.

media/image4.jpeg

Figure 4.

A method for evaluating and achieving agility.

First, identify the agility drivers on the basis of a survey of the business operation environment, determine the agility-level needs and identify the requirements for measuring the capabilities, and select the required providers for assessment. Second, apply the relationship matrix to link and analyze the fuzzy average relation-weight of the capabilities and providers. Third, synthesize the fuzzy ratings and average relation-weights of the capabilities to obtain the fuzzy-agility-index (FAI) of the enterprise and match the FAI with an appropriate linguistic term to label the agility level. Fourth, synthesize the fuzzy ratings and average relation-weights of the providers to obtain the fuzzy merit-relation-value index for each and rank them to identify the major barriers to enable managerial proactive implementation of appropriate ameliorating measures, a stepwise procedure for which follows.

  1. Form a self-assessment committee.

  2. Collect and survey data or information to identify the agility drivers, determine the needed capabilities and select the required providers for assessment.

  3. Select the preference scale for measurement.

  4. Apply the relationship matrix and use linguistic measurement to evaluate the agility attributes, relationship-levels and prepare a translation.

  5. Analyze the fuzzy average relation-weights of the capabilities and providers.

  6. Aggregate the fuzzy ratings and average relation-weights of capabilities into an FAI.

  7. Match the FAI with an appropriate linguistic agility level.

  8. Analyze the agility and offer suggestions.

A. Self-Assessment Committee

The essentials of an agile enterprise consist of integration of strategies, personnel, processes, networks and information systems. For knowledge acquisition to be successful, it is important that a variety of experts from different functions be chosen. Such a selection ensures that not only the complete domain is covered, but also that no single aspect of the business receives a greater emphasis within the final system.

B. Preparation for Assessment

Before assessing, the committee must survey the changes in the business operation environment and examine the organization’s capability. On the basis of the external environmental survey and internal capability assessment, the committee can identify the main drivers, determine the level of agility needed and the capabilities of the enterprise in response to unpredictable changes, and select the agility-enabled attributes that are the means by which the so-called capabilities can be achieved.

C. Preference Scale System

Due to impreciseness and ambiguity in the criteria, which exist in the evaluation of agility, a precision-based evaluation may not be practical. Thus, the ratings of the attributes and the relationship-level assessment are frequently measured in linguistic terms rather than numerical ones.

The ad hoc usage of linguistic terms and corresponding membership functions is characteristic of fuzzy logic. It is notable that many popular linguistic terms and corresponding membership functions have been proposed for assessment [38, 40]. For the sake of convenience and in lieu of elicitation from the assessors, linguistic terms and corresponding membership functions were obtained directly from previous studies, or, on the basis of the needs of cognitive perspectives and available data characteristics, data from previous studies were used as the foundation for modification to meet individual situations and requirements, the results for which more satisfactorily fit users’ needs. Furthermore, it is generally suggested that linguistic levels not exceed nine levels representing the limits of absolute human discrimination [41].

D. Relationship-Matrix Application, Linguistic Measurement, and Translation

In preparation for evaluating agility, the assessors must survey and study the related data or information concerning implementation to gain an understanding of what will be considered in the evaluation.

After studying the data, on the basis of the experts' experience and knowledge, the assessors can directly use the aforementioned linguistic terms to assess the rating which characterizes the merit level of the various factors. Furthermore, the linguistic terms can be used to assess interrelationship level located in the central portion of the relationship matrix, indicating the experts’ perceptions regarding relationships between drivers, capabilities and providers, implemented by direct assignment or indirect pair comparisons.

After the factors are rated and the interrelationship-level evaluated, the fuzzy numbers such as those listed in Table I are used to approximate the linguistic values.

Levels of changeMerit ratingsRelationship-levels
Linguistic variableFuzzy numberLinguistic variableFuzzy numberLinguistic variableFuzzy 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, 0.2 5 , 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. 7 5, 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)E xcellent (E)(0.85, 0.95, 1.0)

Table 1.

Fuzzy numbers to approximate linguistic variable values.

E. Analysis of Fuzzy Average Relation-Weights

Aggregation of the different experts' opinions in group decision-making is important, wherein many methods such as the arithmetical mean, median, and mode can be used. Since the median operation is more robust in a small sample, this method is recommended for aggregating these assessments.

On the basis of the traditional QFD methodology [42] and the definition of the fuzzy weighted average [43], the fuzzy average relation-weight representing the total relationship-levels between a particular column item and the entire list of row items can then be calculated as

FARWACJ=i=1m(FRLADACijFLCADi)/i=1mFLCADi
(1)

where FARWAC j denotes the fuzzy average relation-weight of the j th agility capability to all the agility drivers; FLCAD i denotes the fuzzy level in change of the i th drivers; FRLADAC ij denotes the fuzzy relationship-level between driver i and capability j.

FARWAPk=j=1n(FRLACAPjkFARWACj)/j=1nFARWACj
(2)

where FARWAP k denotes the fuzzy average relation-weight of k th providers to all the agility capabilities; FARWAC j denotes the fuzzy average relation-weight of the j th capability derived from Eq (1); FRLACAP jk denotes the fuzzy relation-level between capability j and provider k.

The calculation of the membership function of a fuzzy weighted average is tedious, as indicated in [44, 45].

F. Aggregation of Fuzzy Ratings and Average Relation-Weights into Fuzzy-Agility Index

Representing the composite agility level of an enterprise, the fuzzy-agility index (FAI) constitutes a fusion of information, i.e., a consolidation of the fuzzy merit of agility capabilities with the fuzzy average relation-weight of the drivers. The higher the FAI of an enterprise is, the higher its agility.

According to the fuzzy weighted average operation [43], the FAI is defined as

FAI=j=1n(FMACjFARWACJ)/i=1mFARWACJ
(3)

where FMACj denotes the fuzzy merit of the j th agility capability and FARWAC j denotes the fuzzy average relation-weight of the j th capability derived from Eq (1).

G. Matching FAI with an Appropriate Linguistic Level

Once the FAI has been compiled, one can further approximate a linguistic label whose meaning is the same as (or closest to) the meaning of the FAI from the natural-language expression set of an agility label (AL).

Several methods for matching the membership function with linguistic terms have been proposed. Three basic techniques include (1) Euclidean distance, (2) successive approximation, and (3) piecewise decomposition. The Euclidean distance method is most frequently utilized because it is the most intuitive form of human perception of proximity [46].

The Euclidean method consists of calculating the Euclidean distance from the given membership function to each functions representing the natural-language agility level expression set. Suppose that the natural-language agility level expression set is AL, U FAI and U ALi are the membership functions of FAI and the natural-language agility level expression, respectively. Then, the distance between the fuzzy number FAI and each fuzzy-number AL i AL can be calculated as

d(FAI,ALi)={xp(UFAI(x)UALi(x))2}1/2
(4)

where p = {x 0 , x 1 , …, x m } [0, 1] so that 0 = x 0 x 1 x m = 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}. Then, the distance from the FAI to each of the members in the set AL can be calculated and the closest natural expression with the minimum distance identified.

H. Analysis and Suggestions

As mentioned in the previous section, an evaluation of agility not only determines the agility of an enterprise but also, most importantly, helps managers identify the principal adverse factors for implementing an appropriate plan to enhance the agility level.

Agility-enabling attributes are supposed to provide and determine the entire agile behavior of an enterprise. To identify the principal obstacles to enhancing the agility level, a fuzzy agility-provider merit-relation-value index (FAPMRVI) combining the merit ratings and the average relation-weights of providers derived from Eq (2) is defined. The lower the FAPMRVI of a factor is, the lower the degree of contribution for the factor.

If the fuzzy average relation-weight is used to calculate FAPMRVI k directly, the high value obtained neutralizes the low merit ratings in the calculation of FAPMRVI; therefore, the actual principal obstacles (low merit rating and high average relation-weight) cannot be identified. If a high value is given to FARWAP k , then [(1, 1, 1) FARWAP k ] becomes a low value. Hence, to elicit the factor with the lowest merit rating and the highest average-relation-weight for each agility provider k, the fuzzy index for FAPMRVI k is defined as

FAPMRVIk=FMAPkFARVAPk
(5)

where FARVAP k = [(1, 1, 1) FARWAP k ]; FMAP k denotes the fuzzy merit of the k th agility provider.

Since fuzzy numbers do not always yield a totally ordered set as real numbers do, all the FAPMRVI k must be ranked. Many methods have been developed to rank fuzzy numbers [40, 47]. Here, the ranking of the fuzzy numbers is based on Chen and Hwang’s left-and-right fuzzy-ranking method [40] since it not only preserves the ranking order but also considers the absolute location of each fuzzy number. The shortcoming of this method is that the ranking score depends on the definition of their fuzzy maximizing and minimizing sets.

In the left-and-right fuzzy-ranking method, the fuzzy maximizing and minimizing sets are, respectively, defined as

Umax(x)={x,0x10,otherwise
(6)
Umin(x)={1x,0x10,otherwise
(7)

When a triangular fuzzy number is given, the FAPMRVI defined as U FAPMRVI R [0, 1] with a triangular membership function. Thu, the right-and-left scores of the FAPMRVI can be obtained, respectively, as

UR(FAPMRVI)=supx[UFAPMRVI(x)Umax(x)]
(8)
UL(FAPMRVI)=supx[UFAPMRVI(x)Umin(x)]
(9)

Finally, the total score of the FAPMRVI can be obtained by combining the left and right scores, being defined as

UT(FAPMRVI)=[UR(FAPMRVI)+1UL(FAPMRVI)]/2
(10)

5. A practical case study

In this section, an agility development project of an international IT products-and-services enterprise in Taiwan is cited to demonstrate the evaluation procedure for this approach.

A. Subject of Case Study

“Enterprise A” is an internationally recognized IT products-and-services company, particularly noted for PCs and notebooks, earning an annual revenue of about US $6.2 billion in 2005. This enterprise employs marketing and service operations across the Asia-Pacific Rim, Europe, the Middle East, and the Americas, supporting dealers and distributors in more than 100 nations. In the 1990’s, the markets for IT products matured; moreover, low-cost production in developing nations grew, thus prompting large multinational firms to simultaneously provide local responsiveness and global integration to in reaction to an uncertain business environment. Such changes profoundly challenged the enterprise. To achieve and sustain global success and satisfy new small-niche markets, this enterprise strived to become a major global supplier to enrich its customers, reduce to-market time, reduce the total cost of ownership, and enhance overall competitiveness.

Since an enterprise has been advocated as the 21st-century operation paradigm, and being perceived as a winning strategy for becoming national and international leader, the corporate management team (executive team) concluded that it wished to achieve an extremely agile enterprise through continuous improvement processes. Thus, an assessment team led by the executive vice president was organized. This team was selected from the most knowledgeable personnel who had mastered the principles of an agile enterprise and whose job it was to investigate and correct problems. The team membership encompassed the vice president of marketing, the general auditor, the global manufacturing manager, the director of human resources, a senior project manager and two consultants for business strategy. Each member brought particular concerns and desires into the decision, which had to be reconciled by consensus, a necessary procedure since all parties would contribute to the success or failure of the project.

B. Commitments of Project

The aim of agility evaluation is to produce a good set of results, from which an agility index is determined for perceptions of the current situation, and another index for the goals toward increasing the agility of the enterprise. Since top-level commitment is essential, 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 enterprise and areas needing improvement for feedback to the management team;

  • To feed opportunities for improvement into the business planning cycle, including corporate objectives; and

  • To develop the process of self-assessment by using the agile enterprise model as an annual component of the business cycle.

C. Evaluation by Fuzzy QFD-Based Algorithm

When enterprise A sets the goal to implement an agile enterprise, the committee had several questions, such as: Precisely what is agility, and how can it be measured? How can both analytical and intuitive understandings of agility be developed in a particular business environment? How can the agility of enterprise A be improved? Answering these questions requires knowledge of what to measure, how to measure it and how to evaluate the results. Moreover, how to integrate drivers, capabilities and providers into alignment must be taken into account if the enterprise is to implement agility. Although important concepts and steps for development formulation have previously been identified, there is still no systematic tool to integrate these concepts. Furthermore, due to the existing ill-defined and ambiguous elements concerning agility factors and their interrelationships, experts can easily differentiate between high, medium, and low; however, 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 use linguistic terms to measure ambiguous events. Since linguistic variables contain ambiguity and a multiplicity of meanings and the information obtained can be expressed as a range in a fuzzy set instead of a single value as in traditional methods, fuzzy logic may be applied in this evaluation context. On the basis of the procedures of the fuzzy QFD-based algorithm, the agility development evaluation was implemented and the goal achieved. The deliberations concerning how to initiate agility development are summarized below:

1) Identify agility drivers, determine capabilities and select providers for assessment. To accurately elicit assessment criteria reflecting the entire set of features of an agile enterprise within a period of ten days, the committee made a series of business-environment changes, as well as trend surveying and analysis, the major content of which included changes in the marketplace, competitive circumstances and criteria; technological innovations and applications; changes in customer requirements; and changes in social factors. Moreover, to facilitate the experts’ holistic understanding of the current situation, two review meetings were held to discuss a series of activities, the major content of which included

  • Enterprise characteristics: enterprise priorities (quality, cost, time, customers satisfaction, etc.), perceived quickness, responsiveness, core business and competencies, as well as specific enterprise problems;

  • Policy and strategy: the key factors prompting the enterprise to change and the strategies adopted;

  • Business structure: organization, process, personnel, information technology and innovative structures providing the capability for achieving agility;

  • Practices: those performed in response to change

On the basis of discussion results, the committee further referred to the factors proposed in previous studies [1, 4, 7, 8, 10, 16-18]. The agility drivers were identified and the capabilities and providers for assessment selected, as shown in Table II. (This Table presents merely what the author assessed to be the most prevalent and meaningful factors for this case study).

DriversCapabilitiesProviders
Growth of niche market (A D 1 )Sensing /Identifying changes and fast response (A C 1 )Multi-skilled and flexible pe rsonnel (A P 1 )
Increasing rate of change in product models (A D 2 )Strategic vision (A C 2 )Workforce skill upgrade (A P 2 )
Product lifetime shrinkage (A D 3 )T echnological ability and appropriate product introduction (A C 3 ) .Q uick new product introduction (A P 3 )
Rapidly changing market (A D 4 )Cost - effectiveness (A C 4 )Response to changing market requirements (A P 4 )
Increasing pressure on cost (A D 5 )Cooperation and operation efficiency and effectiveness (A C 5 )Products with substantial value-addition (A P 5 )
Increasing pressure of global market competition (A D 6 )Product volume/model flexibility (A C 6 )First-time right design (A P 6 )
Decreasing new products time to market (A D 7 )Organization/pe rsonnel flexibility (A C 7 )Trust-based relation s with customers/suppliers (A P 7 )
Quicker delivery time and time to market (A D 8 )Product/service design , delivery alacrity and timeliness (A C 8 )Technology awareness (A P 8 )
I ncreasing q uality expectation (A D 9 )Fast operation time (A C 9 )Skill and knowledge enhanc ement (A P 9 )
Introduction of new soft technologies (software and methods) (A D 10 )Concurrent execution of activities (A P 10 )
Environmental pressures (A D 11 )Information technology and communication (A P 11 )
Empowerment and decentralized decision - making (A P 12 )
Cross - functional team (A P 13 )
Culture of change (A P 14 )

Table 2.

Agility-related factors.

2) Determine the preference scale for measurement. This is based on the needs for cognitive perspectives and available data characteristics and also considers the linguistic terms used in previous studies and modified to incorporate enterprise A situations. Furthermore, after two days of discussion based on a long-standing recognition of the meaning of linguistic values, ultimately the committee selected for assessment the linguistic terms and associated fuzzy numbers listed in Table I.

3) Apply the relationship matrix and use linguistic terms to assess agility attributes and relationship-levels, and translate the linguistic terms into fuzzy numbers. Within a period of six days, a series of brainstorming sessions was held to identify the relationships among the variables. For this, the experts were asked about the mutual relationships among variables (e.g., how a particular variable helps to achieve the others). By using the conclusions in the review meetings and brainstorming session, and on the basis of their experience, knowledge and judgment, the committee members applied the relationship matrix (as shown in Tables III and IV) 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 used the value scale RS = {Very Low [VL], Low [L], Fair [F], High [H], Very High [VH]} to evaluate extent of the relationships between agility drivers and capabilities, as well as the relationship-levels between capabilities and providers; moreover, they used the rating scale R = {Worst [W], Very Poor [VP], Poor [P], Fair [F], Good [G], Very Good [VG], Excellent [E]} to assess the merit ratings of the capabilities and providers. A sample of the linguistic assignment is shown in Tables III and IV. Furthermore, on the basis of the associated relations shown in Table I, fuzzy numbers approximating the linguistic terms and linguistic assignments were translated into fuzzy numbers.

Agility capabilities
AC 1AC 2AC 3AC 4AC 5AC 6AC 7AC 8AC 9
Merits of agility capabilities Level of changeGGGFFVGFPF
Agility driversAD 1VHVHHHFFVHHHVH
AD 2VHHHVHHHHVHHVH
AD 3VHHHVHHHFHHVH
AD 4VHHVHHHHVHHVHVH
AD 5EHHFHVHHHFHH
AD 6VHHVHHVHVHHVHVHVH
AD 7VHVHHHFHHHVHVH
AD 8VHVHHHFHHHVHVH
AD 9HHFHFVHFFHF
AD 10HHHHHFHHHH
AD 11HFHFLFLFFL

Table 3.

Agility capability related to drivers: agile strategies analysis matrix (assigned by general auditor).

Agility providers
AP 1A P 2A P 3A P 4A P 5A P 6A P 7A P 8A P 9AP 10A P 11A P 12A P 13A P 14
Merits of agility providersVGGVGFGGGVGVGGGEFG
Agility capabilitiesAC 1HHVHHHHLHHFHVHHH
AC 2HFHHHHVHHFHHHFVH
AC 3VHVHVHHHVHFHHHVHFHH
A C 4HHHHHHVHHHHHFFH
A C 5HHHVHHVHHHHVHHHVHH
A C 6VHHHVHHHHHHHHHHF
A C 7VHHHVHHHHHHVHHVHHH
A C 8HHHVHHVHHHHVHHHHH
A C 9VHHHVHHVHHHHVHHHVHH

Table 4.

Agility providers related to capabilities: principle obstacle identification matrix (assigned by general auditor).

4) Analyze the fuzzy average relation-weight in the relationship matrix. Before this analysis, the committee used the median operation to integrate the different assignments under the same factors given by different experts. Furthermore, by applying Eqs. (1)-(2), the fuzzy average relation-weights of the agility capabilities and providers can be calculated, respectively. The results are listed in Table V.

A gility capabilit y F uzzy average relation- weights A gility providers F uzzy average relation- weights
Sensing /Identifying changes and respon ding (A C 1 ) .(0.61, 0.76, 0.92)Multi-skilled and flexible pe rsonnel (A P 1 ) (0.60, 0.74, 0.90)
Strategic vision (A C 2 ) .(0.60, 0.76, 0.91)Workforce skill upgrade (A P 2 )(0.55, 0.72, 0.88)
T echnological ability and appropriate product introduction (A C ) .(0.65, 0.79, 0.93)Q uick new product introduction (A P 3 )(0.60, 0.76, 0.91)
Cost - effectiveness (A C 4 ) .(0.61, 0.77, 0.92)Response to changing market requirements (A P 4 )(0.63, 0.78, 0.93)
Cooperation and operations efficiency and effectiveness (A C 5 ) .(0.58, 0.75, 0.91)Products with substantial value-addition (A P 5 )(0.52 0.70, 0.87)
Product volume/model flexibility (A C 6 )(0.54, 0.73, 0.89)First-time right design (A P 6 )(0.62, 0.77, 0.93)
Organization/pe rsonnel flexibility (A C 7 )(0.46, 0.63, 0.76)Trust-based relation s with customers/suppliers (A P 7 )(0.55, 0.73, 0.89)
Product/service design , delivery alacrity and timeliness (A C 8 )(0.67, 0.82, 0.96)Technology awareness (A P 8 )(0.54, 0.72, 0.88)
Fast operation time (A C 9 )(0.65, 0.81, 0.95)Skill and knowledge enhanc ement (A P 9 )(0.60, 0.75, 0.9)
Concurrent execution of activities (A P 10 )(0.60, 0.76, 0.91)
Information technology and communication (A P 11 )(0.60, 0.75, 0.9)
Empowerment and decentralized decision - making (A P 12 )(0.52, 0.71, 0.88)
Cross - functional team (A P 13 )(0.55, 0.73, 0.89)
Culture of change (A P 14 )(0.37, 0.58, 0.78)

Table 5.

Fuzzy average relation-weights of agility capabilities and providers.

5) Aggregate the fuzzy ratings and fuzzy average relation-weights into an FAI. By applying Eq (3), the FAI for enterprise A was obtained as

FAI = (0.37, 0.56, 0.75)

6) Match the FAI with an appropriate linguistic level. Once the FAI was obtained, to identify the agility level, the committee further approximated a linguistic label whose meaning is the same as (or closest to) the meaning of the FAI from the natural-language agility-level (AL) expression set. In this case, the set AL = {Definitely Agile [DA], Extremely Agile [EA], Very Agile [VA], Highly Agile [HA], Agile [A], Slightly Agile [SA], Fairly [F], Slightly Slow [SS], Slowly [S]} was selected for labeling, the linguistics and corresponding membership functions of which are shown in Figure 5.

media/image15.jpeg

Figure 5.

Matching fuzzy agility index with linguistic terms.

Then, by using Eq (4), the Euclidean distance D from the FAI to each member in set AL was calculated:

D(FAI, DA ) =2.0094, D(FAI, EA) = 2.0094, D(FAI, VA) = 1.7277,

D(FAI, HA) = 0.9924, D(FAI, A) = 1.1405, D(FAI, SA) =1.8168,

D(FAI, F) =2.0094, D(FAI, SS) =2.0094, D(FAI, S) = 2.0094

Thus, by matching a linguistic label with the minimum D, the agility level of enterprise A can be labeled as “Highly Agile”, as shown in Figure 5.

7) Analyze and suggest. Since the agility index of enterprise A is “Highly Agile” (according to the evaluation), far from the “Extremely Agile” objective, obstacles within the organization can stop or impact the achievement of the company. Agility providers are supposed to enable and determine the entire agile behavior of an enterprise. By applying Eq (5), the fourteen fuzzy agility-provider merit-relation-value indexes (FAPMRVIs) listed in Table VI were obtained.

Agility providersMerit s of agility provider(1.0, 1.0, 1.0) (-) F AR W A P i Fuzzy relation-value index esRanking score s
A P 1(0.3, 0.5, 0.7)(0. 1 , 0. 26 , 0. 4 )(0.0 3 , 0. 13 , 0. 28 )0.1808
A P 2(0.5, 0.65, 0.8)(0. 12 , 0. 28 , 0. 4 5)(0. 06 , 0. 182 , 0. 36 )0.2339
A P 3(0.7, 0.8, 0.9)(0. 09 , 0. 24 , 0. 4 )(0.0 63 , 0. 192 , 0. 36 )0.2391
A P 4(0.5, 0.65, 0.8)(0. 07 , 0. 22 , 0. 37 )(0.0 35 , 0. 143 , 0.2 96 )0.1929
A P 5(0.5, 0.65, 0.8)(0. 13 , 0. 3 , 0. 48 )(0.0 65 , 0. 195 , 0. 384 )0.2478
A P 6(0.3, 0.5, 0.7)(0. 07 , 0. 23 , 0. 38 )(0.0 21 , 0. 115 , 0. 266 )0.1681
A P 7(0.5, 0.65, 0.8)(0. 11 , 0. 27 , 0. 45 )(0. 055 , 0 .176 , 0.3 6 )0.2305
A P 8(0.7, 0.8, 0.9)(0. 1 2, 0. 28 , 0. 46 )(0. 084 , 0. 224 , 0. 414 )0.2722
A P 9(0.5, 0.65, 0.8)(0. 1 , 0. 25 , 0. 4 )(0. 05 , 0. 163 , 0. 32 )0.2115
A P 10(0.5, 0.65, 0.8)(0. 09 , 0. 24 , 0. 4 )(0.0 45 , 0. 156 , 0. 32 )0.2077
A P 11(0.5, 0.65, 0.8)(0. 1 , 0. 2 5, 0. 4 )(0.0 5 , 0. 163 , 0. 32 )0.2115
A P 12(0.5, 0.65, 0.8)(0. 12 , 0. 29 , 0. 48 )(0.0 6 , 0. 189 , 0. 384)0.2444
A P 13(0.3, 0.5, 0.7)(0. 11 , 0. 27 , 0. 45 )(0.0 33 , 0.1 35 , 0. 315 )0.1947
A P 14(0.3, 0.5, 0.7)(0. 22 , 0. 42 , 0. 63 )(0.0 66 , 0. 21 , 0. 441 )0.2709

Table 6.

Fuzzy merit-relation-value indexes of agility providers.

Moreover, by applying Eqs (6)-(10), the FAPMRVIs were defuzzified, as listed in Table V. These indices represent the effect of each provider contributing to the agility level of enterprise A. On the basis of the Pareto principle, the committee decided to focus their resources on a few critical factors and sets a scale of 0.2 as the management’s threshold for identifying the factors for improvement. Subsequently, as shown in Table VI, 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. These providers represent the most significant contributions for enhancing the agility of the enterprise. In connection with the weakest providers within the organization, the committee suggested that an action plan be implemented to improve the adverse providers and to enhance the agility level of the company.

After five years and ten cycles of continuous implemented improvement, the agility index of enterprise A has risen close to the “Extremely Agile” level; moreover, the managers are able to capture information on demand immediately from all over the world to make rapid and appropriate decisions to respond more efficiently and effectively to customers. The tangible benefits are the mean lead-time for responding to customers’ demands reduced by approximately 37% under the same inventory level; sales-average increased by 11%, 23%, 27%, 17% and 19% during the five years; an ascent from ninth of fourth position in the world market, especially boosted by becoming the leading brand of PCs and notebooks in the European market.

6. Discussion and conclusions

The agility of an enterprise is perceived as the dominant competitive vehicle. This report has highlighted the following questions: How close is the enterprise to becoming agile? How can the enterprise effectively improve its agility? Deploying and integrating agility providers, capabilities and drivers and transforming them into strategic competitive edges are critical for an enterprise to achieve agility. Although important concepts and steps for achieving agility have been identified, there is still no systematic tool for integrating these steps. Most of the existing approaches for agility development are structural in nature. Also, conventional (crisp) evaluation approaches which are unsuitable and ineffective for handling situations which by nature lead to complexity and vagueness have been evaluated. To compensate for these limitations, a QFD-based framework to logically integrate the agility provider, capability and driver has been proposed. The methodology provides a systematic structure for translating the agility drivers in the business environment into capabilities needed and subsequently for determining the requirements of agility-enabled attributes. In addition a fuzzy agility index (FAI) composed of agility capability ratings and its relation-weights with drivers has been developed for agility measurement in an enterprise. This report has also described how the proposed approach was applied to develop agility in a Taiwanese PC enterprise. Through development and evaluation, it has been shown that the proposed framework and procedures can enhance the agility of an enterprise, as well as ensure a competitive edge.

This method has been developed from the QFD concept and adapted for a PC enterprise which served as an initial case study for validating the model and approach. The enterprise and managers involved in the case study were generally pleased with the approach. This work provides potential value to practitioners by offering a rational structure to logically integrate different elements at various stages of strategic planning. The uncertainty and vagueness of assessment of each attribute and relationship have been addressed to assure relatively realistic information. An unprecedented application of the QFD and fuzzy logic has been demonstrated to researchers.

Since the case study has demonstrated the usefulness of the model for business strategic planning, it is hoped that more managers will be encouraged to adopt this method. However, neither a single case study nor several necessarily provide a true measure of the relative performance and success of this model. Further research should be done to bring this method to maturity and to compare the efficiency of the method in different types of planning (such as information-strategy, marketing, product-roadmap, knowledge-management, etc.). Moreover, this approach does not focus on finding an optimal deployment but merely addresses prioritizing agility providers. For further research, a goal-programming model can be developed to select in greater detail the combination of agility capabilities and providers which results in optimal levels of agility, subject to cost and other enterprise constraints.

It is acknowledged that the evaluation levels and members involved in any particular implementation will be different, depending on the firm involved. The agility drivers and entrepreneurial objectives and strategies vary from firm to firm. For example, enterprises in high-tech industries, stressing competitive advantage through innovation, may have decided on agility capabilities and providers differently from firms in traditional industries seeking to compete in flexibility, global sourcing and low-cost providers.

Furthermore, according to the comments from the previous case, this approach resolves some of the problems in traditional methods of strategic business planning, having several advantages when compared to previous methods:

  1. This method provides a structured procedure for identifying the agility drivers in a business environment, thereby deploying capabilities needed to finally determine the providers that will support or enhance the agility of the enterprise. Furthermore, the case study demonstrated that having providers align with strategy and drivers ensures that the providers can cope with strategic direction and provide a competitive edge for the enterprise.

  2. This method gives the analyst more convincing and reliable results. The FAI was expressed in a range of values, providing an overall description of the agility of an enterprise and ensuring that the decision made in the evaluation is not biased. As an example, an agility index having a fuzzy value (0.37, 0.56, 0.75) indicates that the agility level is closer to “Highly Agile,” but also not far away from “Agile.”

  3. This method provides a guiding, dynamic document linking the business strategy of a firm with its environment and outlines details for implementation through continuous process improvement and total quality management.

  4. This method provides a first step in preventing a majority of inappropriate assessments and also expedites the eventual financial analysis by highlighting the most important benefits and drawbacks for formulating a comprehensive plan for improvement.

Finally, there are some limitations to the fuzzy-logic approach. The membership function of natural language expression depends on the managerial perspective of the experts, who must be at a strategic level in the enterprise to evaluate the importance of all aspects such as strategy, marketing and technology. Furthermore, competitive situations and requirements vary from one enterprise or industry to another; hence, a company must establish its unique membership function appropriate to its own specific environment and considerations. Moreover, the computation of a fuzzy weighted average is still complicated and not easily appreciated by managers. Fortunately, this calculation has been computerized to increase accuracy while reducing both computation time and the possibility of errors.

References

1 - H. Sharifi, Z. Zhang, 1999 “Methodology for achieving agility in manufacturing organization: An introduction,” International Journal of Production Economics, 62 7 22 , 1999.
2 - A. Agarwal, R. Shankar, M. K. Tiwari, 2007 “Modeling agility of supply chain,” Industrial Marketing Management, 36 443 457 , 2007.
3 - Y. Y. Yusuf, A. Gunasekaran, E. O. Adeleye, K. Sivayoganathan, 2004 “Agile supply chain capabilities: Determinants of competitive objectives,” European Journal of Operational Research, 159 379 392 , 2004.
4 - Z. Zhang, H. Sharifi, 2007 “Towards theory building in agile manufacturing strategy-A taxonomical approach,” in press, 2007.
5 - H. Ismail, I. Reid, J. Mooney, J. Poolton, I. Arokiam, 2006 “How small and medium enterprises effectively participate in the mass customization game,” IEEE C. T. Lin, H. Chiu and Y. H. Tseng, “Agility evaluation using fuzzy logic,” International Journal of Production Economics, 101 353 368 , 2006.
6 - Y. Y. Yusuf, M. Sarhadi, A. Gunasekaran, 1999 “Agile manufacturing: The drivers, concepts and attributes,” International Journal of Production Economics, 62 33 43 , 1999.
7 - J. M. Sharp, Z. Irani, S. Desai, 1999 “Working towards agile manufacturing in the UK industry,” International Journal of Production Economics, 62 155 169 , 1999.
8 - S. M. R. James-Moore, 1997 “Agility is easy, but effective agile manufacturing is not,” in The Institution of Electrical Engineers. London: IEE. 1997.
9 - Y. Y. Yusuf, J. Ren, N. D. Burns, 2001 “A method for evaluating enterprise agility- An empirical study,” in 16th International Conference on Production Research, Prague, Czech Republic, 2001.
10 - Z. Zhang, H. Sharifi, 2000 “A methodology for achieving agility in manufacturing organizations,” International Journal of operations and Production Management, 20 4 496 512 , 2000.
11 - K. Preiss, S. L. Goldman, R. N. Nagel, 1996 Cooperate to Compete: Building Agile Business Relations. Van Nostrand, Reinhold, New York, 1996.
12 - H. Sharifi, Z. Zhang, 2001 “Agile manufacturing in practice: Application of a methodology,” International Journal of operations & Production Management, 21 5-6 , 772 794 , 2001.
13 - M. Jackson, C. Johansson, 2003 “An agility analysis from a production system perspective,” Integrated Manufacturing Systems, 14 6 482 488 , 2003.
14 - A. Gunasekaran, 1998 “Agile manufacturing: Enables and an implementation framework” International Journal of Production Research, 36 5 1223 1247 , 1998.
15 - M. A. Youssuf, 1993 “The impact of the intensity level of computer-based technologies on quality,” International Journal of Operations & Production Management, 14 4 4 25 , 1993.
16 - M. M. Weber, 2002 “Measuring supply chain agility in the virtual organization,” International Journal of Physical Distribution & Logistical Management, 32 7 577 590 , 2002.
17 - L. M. Meade, K. J. Rogers, 1997 “Enhancing a manufacturing business process for agility,” in The Portland International Conference on Management and Technology, Portland, 1997.
18 - J. Ren, Y. Y. Yusuf, N. D. Nagel, 2000 “A prototype of a measurement system for an agile enterprise,” in The Third International Conference of Quality Reliability Maintenance, Oxford, UK, 2000.
19 - S. L. Yang, T. F. Li, 2002 “Agility evaluation of mass customization product manufacturing,” Journal of Materials Processing Technology, 129 640 644 , 2002.
20 - N. C. Tsourveloudis, K. P. Valavanis, 2002 “On the measurement of enterprise agility,” International Journal of Intelligent and Robotic systems, 33 3 329 342 , 2002.
21 - P. T. Kidd, 1994 Agile Manufacturing: Forging New Frontiers. Reading, MA: Addison-Wesley, 1994.
22 - S. H. Kim, D. H. Jang, D. H. Lee, S. H. Cho, 2000 “A methodology of constructing a decision path for IT investment,” Journal of Strategic Information Systems, 9 17 38 , 2000.
23 - H. Moskowitz, K. J. Kim, 1997 “QFD optimizer: A novice friendly quality function deployment decision support system for optimizing product design,” Computer and Industrial Engineering, 32 641 655 , 1997.
24 - L. L. Machacha, P. Bhattacharya, 2000 “A fuzzy logic-based approach to project selection,” IEEE Transactions on Engineering Management 47 1 65 73 , 2000.
25 - R. A. Roussel, K. N. Saad, J. J. Erickson, 1991 Third Generation R&D: Managing the Link to Corporate Strategy. Boston, MA: Harvard Business Press, 1991.
26 - K. Wang, C. K. Wang, C. Hu, 2005 “Analytic hierarchy process with fuzzy scoring in evaluating multidisciplinary R&D projects in China,” IEEE Transactions on Engineering Management, 52 1 119 129 , 2005.
27 - Ghalia. M. Ben, P. P. Wang, 2000 “Intelligent system to support judgmental business forecasting: The case of estimating hotel room demand,” IEEE Transactions on Fussy Systems 8 4 350 397 , 2000.
28 - C. T. Lin, C. T. Chen, 2004 “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 1 132 142 , 2004.
29 - L. H. Chen, T. W. Chiou, 1999 “A fuzzy credit-rating approach for commercial loans: A Taiwanese case,” Omega, 27 4 407 419 , 1999.
30 - P. C. L. Hui, K. C. C. Chan, K. W. Yeung, F. S. F. Ng, 2002 “Fuzzy operator allocation for balance control of assembly lines in apparel manufacturing,” IEEE Transactions on Engineering Management, 49 2 173 180 , 2002.
31 - M. T. Chu, J. Z. Shyu, G. H. Tzeng, R. Khosla, 2007 “Using nonadditive fuzzy integral to assess performances of organizational transformation via communities of practice,” IEEE Transactions on Engineering Management, in press, 2007.
32 - A. Kumar, J. Motwani, 1995 “A methodology for assessing time-based competitive advantage of manufacturing firms,” International Journal of Operations and Production Management, 15 2 36 53 , 1995.
33 - S. L. Goldman, R. N. Nagel, K. Preiss, 1995 Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer. Van Nostrand Reinhold, New York, 1995.
34 - NGM Project Office, 1997 “NGM: Next generation manufacturing, a framework for action,” in K, J. G. Kosanke Ed, Enterprise Engineering and Integration: Building International Consensus. Springer-Verlag, Berlin, 1997.
35 - Y. Y. Yusuf, M. Sarhadi, A. Gunasekaran, 1999 “Agile manufacturing: The drivers, concepts and attributes,” International Journal of Production Economics, 62 33 43 , 1999.
36 - J. Ren, Y. Y. Yusuf, N. D. Burns, 2001 “Organizational competitiveness: Identifying the critical agile attributes using principal component analysis,” in 16th International Conference on Production Research, Prague, Czech Republic, 2001.
37 - W. Karwowski, A. Mital, 1986 “Applications of approximate reasoning in risk analysis,” in Applications of Fuzzy Set Theory in Human Factors. W. Karwowski and A. Mital Eds, Netherlands, Amsterdam, 1986.
38 - D. Dubois, H. Prade, 1988 Possibility Theory-An Approach to Computerized Processing of Uncertainty. New York: Plenum, 1988.
39 - S. J. Chen, C. L. Hwang, 1992 Fuzzy Multiple Attribute Decision Making Methods and Applications. Berlin Heidelberg: Springer-Verlag, 1992.
40 - G. A. Miller, 1965 “The magic number seven, plus or minus seven,” Psychological Review, 63 81 97 , 1965.
41 - V. Bouchereau, H. Rowlands, 2000 “Quality function deployment: The unused tool,” Engineering Management Journal, February, 45 52 , 2000.
42 - T. S. Liou, M. J. Wang, 1992 “Fuzzy weighted average: An improved algorithm,” Fuzzy Sets and Systems, 49 307 315 , 1992.
43 - D. H. Lee, D. Park, 1997 “An efficient algorithm for fuzzy weighted average,” Fuzzy Sets and Systems, 87 39 45, 1997.
44 - C. Kao, S. T. Liu, 2001 “Fractional programming approach to fuzzy weighted average,” Fuzzy Sets and Systems, 120 435 444 , 2001.
45 - H. W. Guesgen, J. Albrecht, 2000 “Imprecise reasoning in geographic information systems,” Fuzzy Sets and Systems, 113 121 131 , 2000.
46 - K. H. Lee, J. H. Lee, 1997 “A method for ranking fuzzy numbers and its application to decision-making,” IEEE Transactions on Fuzzy Systems, 7 6 677 685 , 1997.