EFQM Model by QFD Approach-2011

Expert Systems with Applications 38 (2011) 9633–9647

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Expert Systems with Applications

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Selection effective management tools on setting European Foundation for QualityManagement (EFQM) model by a quality function deployment (QFD) approach

Same Yousefie a,⇑, Mahmood Mohammadi b, Jalal Haghighat Monfared b

a Islamic Azad University, Central Branch of Tehran, Faculty of Management, Tehran, Iranb Islamic Azad University, Central Branch of Tehran, Department of Industrial Management, Tehran, Iran

a r t i c l e i n f o

Keywords:Management toolsEuropean Foundation for QualityManagement (EFQM)Quality function deployment (QFD)Fuzzy analytic hierarchy process (FAHP)Entropy methodSymmetrical triangular fuzzy numbers(STFN)

0957-4174/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.eswa.2011.01.166

⇑ Corresponding author. Address: P.O. Box: 14515Iran. Tel.: +98 9122490306.

E-mail addresses: [email protected], sam.yous

a b s t r a c t

EFQM Excellence model literature indicates that using the management tools that are relevant to theorganization’s needs has become a strategic issue for companies in today’s competitive environment.By choosing and applying the best management tools among too many management tools, companiescan improve their performances and then increase customer satisfaction and gain market shares. Theaim of this research is to propose an original approach for the management tools selection based onthe quality function deployment (QFD) approach, a methodology which has been successfully adoptedin new products development. Specifically, the research addresses the issue of how to deploy the houseof quality (HOQ) to effectively and efficiently improve management tools selection processes and thuscompany satisfaction about its excellence achievement. Fuzzy logic is also adopted to deal with thevagueness nature of the qualitative linguistic judgments required in the proposed HOQ. The model of thisresearch has been tested by means of a real case application, which refers to an Iranian company oper-ating in the automotive industry in this case the mixture of 15 categories of management tools with fiveEFQM enabler criteria has been characterized by using of the research model. And also the test of thehypothesis of this research has been done by using spearman correlation coefficient.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Since its creation in 1991, the main purpose of the EuropeanQuality Award (EQA) has been to recognize the organizationalexcellence in European companies. The EFQM excellence modelis the framework behind this award and it has clearly becomethe most commonly applied model in Europe for total quality man-agement (TQM) (Westlund, 2001). Although in organizationalpractice the use of the EFQM excellence model is practicallyunquestioned, some uncertainties still remain at the academiclevel, mainly related to its implementation and assessment ofaccomplishing of its criteria in the organizations.

The EFQM model constitutes a non-prescriptive frameworkthat assumes there are different approaches to achieving sustain-able excellence (Ghobadian & Woo, 1996) that derives in the exis-tence of multiple interpretations around its implementation.However, it is made up of certain notions and ideas about thegeneral relationships between its elements that have still notbeen demonstrated empirically (Bou-Llusar et al., 2005). In this

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sense, the logic behind the model is that by improving howthe organization operates – the ‘‘management tools’’ – there willbe an inevitable improvement in the ‘‘results’’. It means thatmanagement tools are the main part of EFQM excellence modeland the fact is, achieving excellence is depend on using the bestmanagement tools that are adopt with the organizations needsfor excellence and have a high level of performance in using ofthose management tools.

Previous studies have also emphasized the need to using man-agement tools in developing the excellence in organization (Leon-ard & Aadam, 2002). As shown in Fig. 1, Assume EFQM model as apyramid that the fundamental concept and the criteria of EFQMmodel are in the first and the second level of this pyramid, and alsomanagement tools as the third level of this pyramid (EuropeanFoundation for Quality Management, 1999).

So, identifying and using best management tools according toorganization’s needs in setting EFQM model and achieving resultsin organizations are so important.

The purpose of the current research is to represent a qualityfunction deployment (QFD) model with both crisp and fuzzyapproaches for the linkages between the EFQM criteria and man-agement tools, and use of this model for identifying and prioritiz-ing management tools that are so effective and match withorganizational needs for excellence. This aim can be expressedthrough the following question.

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Fundamental concept

Criteria

Management tools

Fig. 1. Three level of EFQM model.

9634 S. Yousefie et al. / Expert Systems with Applications 38 (2011) 9633–9647

How organizations can identify, prioritize and select manage-ment tools that are effective on setting EFQM model inorganizations?

The answers to this question would facilitate the understandingof what are the most important management tools for an organiza-tion that could the improve results, and thus would give someideas about the areas where the organizations should concentratetheir efforts in the management systems, thus allowing a betterimplementation of the EFQM model.

Voice of Organization (VOO)

(4)

Relation between organizations needs (WHATs) and technical

attributes for excellence (HOWs)

(1)Organization

Needs for ecnellecxE

(ONE)

(WHATs)

(2)Prioritizing

of organization

needs for excellence

(3)Technical attributes for

Excellence (TAE)

(5)Prioritizing of technical attributes

for excellence

House of Excellence(HOE)

Fig. 2. Conceptual model of the research.

2. Research questions and hypothesizes

The preceding section highlights how the EFQM excellencemodel is based on the assumption that management tools drivethe results and how both enabler criteria and management toolsare themselves interrelated. Nevertheless, the EFQM excellencemodel is a non-prescriptive framework that allows organizationsthat adopt different approaches to achieve excellence in results.However, the full benefit of the model is obtained when organiza-tions develop and use best management tools that they are matchto their needs and have effect on enablers in a way that allowsthem to obtain optimal results. This premise leads to our main re-search question, aimed at propose a QFD method for identifying,prioritizing and selection of effective management tools on settingEFQM model in organization, and making comparison betweenboth crisp and fuzzy approaches in using QFD method. This re-search question is aimed at finding out how enablers should beranked to maximize their influence over ranking the using ofmanagement tools. In other words, we attempt to analyze whatis the appropriate method or model within the management toolsdomain that leads to a maximum improvement in a given excel-lence profile.

This main question can be breakdown in to the four questionsand tow hypothesizes consist of following items.

Questions:

1. What are the effective criteria on setting EFQM excellencemodel in an organization?

2. What are the ranking of effective criteria on setting EFQM excel-lence model in an organization (the research case)?

3. What are the effective management tools on setting EFQMexcellence model in an organization?

4. What are the ranking of effective management tools on settingEFQM excellence model in an organization (the research case)?

In according to the fuzzy QFD methods and the literature of thisresearch, the following Hypothesizes were set:

1. The ranking of effective criteria on setting EFQM model in anorganization (the research case) are the same in crisp and fuzzyapproaches.

2. The ranking of effective management tools on setting EFQMexcellence model in an organization (the research case) arethe same in crisp and fuzzy approaches.

3. Research methodology

3.1. Conceptual model of the research

The conceptual model of this research is an extension of a qual-ity function deployment (QFD) model in EFQM excellence modeland management tools selection domain. The model of this re-search can considered as Fig. 2. It should notice that, it is a firsttime that a QFD model has been used in EFQM excellence modeland management tools selection filed.

As shown in Fig. 2, it can consider that organization is as a cus-tomer. But in this model a customer looking for the excellence, andthe needs or criteria of this customer are EFQM enabler criteria. Onthe other hand, the technical attributes for satisfying this customeris the effective management tools on setting EFQM model. So, toachieving excellence, organization has some needs or organiza-tional needs for excellence (ONE) and for satisfying these criteria,organization determine some technical attributes for excellence(TAE). The voices of organization (VOO) about the excellenceshould be hear and answered. So now these criteria and technicalattributes should be prioritizing according to the relationship be-tween themselves. Finally, by use of these prioritized excellencecriteria and technical attributes for excellence, the organizationcan more concentrate on its core competences and has a basisfor its excellence programming.

The QFD model of this research has named house of excel-lence (HOE), because this model basically looking for the excel-lence improvement in organization by prioritizes the effectivemanagement tools on setting EFQM excellence model in organi-zation. As it is known, the house of quality (HOQ) in standardQFD model is for improving the quality of the products accord-ing to the customer’s criteria about the products, but here, orga-nization is as a customer that has some needs for excellence andthese needs should be satisfied by technical attributes for excel-lence (TAE).

3.2. Descriptions of the HOE model

According to the above preparations, our proposed HOE model(Fig. 3) can be described as follows 9-step procedure. Thesedescriptions, both qualitative and quantitative, are based on theideas from Chan and Wu (2005).

Step 1. Identify organization needs for excellence (WHATs): Theproducing company should know what organization need forexcellence is important for the company; otherwise you cannot

Step 5 Generate Technical

Attitude for excellence (HOWs)

Step 6 Determine

Relationships Between WHATs and HOWs

Step 2 Determine Relative

Importance Ratings of WHATs

Step 3 Identify Competitors, Conduct organization Competitive Analysis

& Set organization

Performance Goals for WHATs

Step 1 Identify

Organization Needs for excellence (WHATs)

Step 4 Determine

Final Importance Ratings of WHATs

Step 7 Determine Initial

Technical Ratings of HOWs

Step 8 Conduct Technical

Competitive Analysis & Set Technical Performance

Goals for HOWs

Step 9 Determine Final Technical

Ratings of HOWs

Fig. 3. House of excellence (HOE): a 9-step model.

S. Yousefie et al. / Expert Systems with Applications 38 (2011) 9633–9647 9635

know how to satisfy your organization and thus how to keep yourbusiness successful and achieve excellence. Available methods tocollect organization needs include focus group, individual inter-views, listening and watching, and using existing information. Itis suitable and economical to gather organization needs throughfocus group (American Supplier Institute, 1994). Grouping relatedorganization needs into a category is helpful in analyzing theneeds. Affinity Diagram (Cohen, 1995), a method of arranging ran-dom data into natural and logical groups, can be used to determineorganization needs. Cluster analysis can also be used for this pur-pose (Griffin & Hauser, 1993). Usually organization needs can beorganized as a tree-like structure with an increasing number ofitems moving from left/top (higher levels) to right/bottom (lowerlevels). Suppose that, through appropriate ways, K experts havebeen selected and M organization needs have been identified basedon the opinions of these K experts. The M organization needs aredenoted as W1, . . ., WM. These needs could be classified into somemeaningful categories according to practical situation.

Step 2. Determine the relative importance ratings of organizationneeds for excellence: Organization needs for excellence (WHATs)usually are of different degrees of importance and it is a commonpractice for the company to focus more on the important WHATs.The relative importance of the WHATs is usually expressed as a setof ratings that can be determined by letting the experts reveal theirperceptions on the relative importance of the WHATs and thenaveraging their perceptions. The appropriate ways of obtaining ex-perts’ perceptions are by analytic hierarchy process (AHP). AHP is

very poor poor neutral good

1 2 3 4 5 6 7 8

[0,2] [1,3] [2,4] [3,5] [4,6] [5,7] [6,8] [7,9

one of the most useful multi criteria decision making methodsfor rating alternatives.

Step 3. Identify competitors and conduct organization competi-tive analysis: Competitors who produce the similar productsshould be identified by the company under study. Knowing thecompany’s strengths and constraints in all aspects of excellenceand in comparison with its main competitors is essential for acompany if it wishes to improve its competitiveness in the rele-vant markets. This kind of information can be obtained by askingthe experts to rate the relative performance of the company andits competitors on each WHAT and then to aggregate the expert’ratings. Useful ways of conducting this kind of comparison anal-ysis are also via questioner. Denote the company in question byC1. Suppose that L � 1, competitors are identified, denoted asC2, . . ., CL. Then the K experts are requested to provide their per-ceptions on the relative performance of these L companies’excellence’s criteria of the similar type in terms of the M organi-zation needs. Suppose that expert k supplies a rating xmlk oncompany C1’s performance in terms of Wm using scale (2), wherexmlk is one of the nine crisp numbers or STFNs in scale (1). Thenthe performance rating of company C1 on organization needs forexcellence Wm is given as:

Xml ¼ ðXml1 þ Xml2 þ � � � þ XmlKÞ=K ¼XK

K¼1

Xmlk=K;

m ¼ 1;2; . . . ;M; l ¼ 1;2; . . . ; L ð1Þ

Very good

9

] [8,10]

ð2Þ


Thus, the companies’ performance ratings on the customer needscan be denoted by an M�L matrix, called customer comparisonmatrix:

Based on this X information, experts competitive priority ratings onthe WHATs for the producing company C1 can be obtained, ase = (e1, e2, . . ., em) where em is company C1’s priority rating on cus-tomer need Wm. This set of priority ratings derived by the moreobjective entropy method as introduced in the Appendix.

very weak weak moderate relation strong Very strong

1 2 3 4 5 6 7 8 9

[0,2] [1,3] [2,4] [3,5] [4,6] [5,7] [6,8] [7,9] [8,10]

ð4Þ

According to company C1’s current performance on the WHATsin relation to its competitors’ performance, performance goals onthe WHATs can be set for the company. These goals should beset competitively and realistically by the company, which is ahighly strategically activity involving many considerations fromrelevant management. Assume that for organization need Wm, aproper performance goal am has been set according to scale (1).Thus the company has a goal performance vector in terms of theorganization needs, denoted as a = (a1; a2, . . ., aM). In most cases,each goal performance level should not be lower than current per-formance level, implying the need or desire for further improve-ment. From this we can also set the company’s improvementratio for Wm as um = am/xml. It is obvious that the higher theimprovement ratio, the more the company should work on theWHAT, and thus the more important the WHAT for the company.

Step 4. Determine the final importance ratings of customer needs:organization needs for excellence with higher relative importanceperceived by experts and higher competitive priorities andimprovement ratios should receive higher attention. Thus, organi-zation needs Wm’s final importance rating for the company isdetermined jointly by its relative importance gm, competitive pri-ority em and improvement ratio um as:

fm ¼ um � gm � em; m ¼ 1;2; . . . ;M ð3Þ

WHATs with high such final ratings indicate both importance andpotential business benefit to the company. Of course, a (weighted)sum of um, gm and em can also produce a reasonable fm, if preferred.In any case, we will denote the final importance ratings for the orga-nization needs as a vector, f = (f1, f2, . . ., fM).

Step 5. Generate technical measures (HOWs): After organizationreveal it needs for the product, the company’s excellence team (ex-perts) should develop a set of HOWs to capture the organizationneeds. HOWs could be generated from current organization’s stan-dards or selected by ensuring through cause–analysis that theHOWs are the first-order causes for the WHATs (Hauser & Clausing,1988). Assume that N technical attitude have been developed, de-noted as H1, H2, . . ., HN. Their measurement units and improvingdirections should also be determined, which is usually easy to doand important for the company to conduct technical competitiveanalysis for the HOWs.

Step 6. Determine the relationships between HOWs and WHATs:This is an important work in HOE/QFD which is performed care-fully and collectively by experts. The relationship between aHOW and a WHAT is usually determined by analyzing to whatextent the HOW could technically related to and influence theWHAT. All these relationships form a matrix with the WHATsas rows and the HOWs as columns. It is suitable to complete thismatrix in a column- or HOW-wise manner since once a HOW isdefined we usually begin establishing to what extents it relatesto the WHATs (American Supplier Institute, 1994). Let the rela-tionship value between technical attitude Hn and organization’sneed Wm be determined as rmn according to scale (4). Then wecan form the following relationship matrix between the HOWsand the WHATs:

Step 7. Determine initial technical ratings of HOWs: Initial techni-cal ratings of HOWs are decided by two factors, final importanceratings of WHATs and the relationships between the HOWs andthe WHATs. These ratings indicate the basic importance of theHOWs developed in relation to the WHATs. They are usually com-puted using the simple additive weighting (SAW) method. That is,for technical attitude Hn, its initial technical rating is computed asthe following simple weighted average over its relationships withthe WHATs:

tn ¼ f1 � r1n þ f2 � r2n þ � � � þ fM � rMn ¼XM

m¼1

fm � rmn;

n ¼ 1;2; . . . ;N: ð5ÞOther methods to obtain comprehensive ratings for a set of choicesin relation to a number of performance criteria, such as the tech-nique for order preference by similarity to ideal solution (TOPSIS)(Yoon & Hwang, 1995) and the operational competitiveness rating(OCRA) procedure (Parkan & Wu, 2000), can also be used to com-pute initial technical ratings (Chan & Wu, 1998). We will denote,in any case, the HOWs’ initial technical ratings by a vector,t = (t1, t2, . . ., tN).

Step 8: Perform technical competitive analysis: Although sometechnical parameters and know-HOWs of the competitors’ organi-zation cannot be easily obtained and some may even be kept con-fidential, the producing company should make every effort toacquire this information and failing to do so may result in an unfa-vorable position for the company in the market place. In case of ex-treme difficulty in obtaining the technical attributes for excellenceof the competitors’ on some HOWs, careful technical assessmentsshould be made to give reliable scores (in a suitable scale such as4) representing the technical performance of the competitors’products on the said HOWs.

Let the technical parameter or performance score of companyC1’s excellence needs on technical attitude Hn be determined as

Table 1Quantitative descriptions of HOE model.

Step 5Technical Measures (HOWs): H1 H2 ... HN

Step 6Relationships BetweenWHATs and HOWs (R): H1 H2 ... HN

W1 r11 r12 ... r1N

W2 r21 r22 ... r2N

… ... ... ... ... WM rM1 rM2 ... rMN

Step 2RelativeImportanceRatings (g):

g1

g2

...gM

Step 3Organization Competitive analysis(X) C1 C2 ... CL (e) (a) (u)W1 x11 x12 ... x1L e1 a1 u1

W2 x21 x22 ... x2L e2 a2 u2

... ... ... ... ... ... … …WM xM1 xM2 ... xML eM aM uM

Step 1OrganizationNeeds for excellence(WHATs):

W1

W2

...WM

Step 4FinalImportanceRatings (f):

f1

f2

...fM

Step 7Initial Technical Ratings (t): t1 t2 ... tN

Step 8Technical CompetitiveAnalysis (Y): H1 H2 ... HNC1 y11 y21 ... yN1C2 y12 y22 ... yN2... ... ... ... ...CL y1L y2L ... yNL(z) z1 z2 ... zN(b) b1 b2 ... bN(v) v1 v2 ... vN

Step 9Final Technical Ratings (s): s1 s2 ... sN


ynl. Then we can form the technical comparison matrix of the com-panies’ excellence needs on the HOWs:

From this Y information technical competitive, priority ratings onthe HOWs can be obtained for the producing company using the en-tropy method as described in the Appendix. We will denote theseratings as a vector, z = (z1, z2, . . ., zN), where zn represents the com-pany’s technical competitive priority with respect to Hn. Based onthe above matrix Y, company C1 could also set performance goalson the HOWs. It should be noted that these goals are different fromdesign specifications. Essentially they represent levels of perfor-mance on the HOWs which the company believes is required forits level of excellence to be of technical competitiveness in therelevant markets in comparison with its competitors. The goalsshould also be reachable according to the company’s technicalresources. Suppose that the company sets a goal performance levelbn on Hn, then we have a technical performance goal vector, b =(b1, b2, . . ., bN). Compared to these goals we can define improvementratios vn’s for the current performance of company on the HOWs:

Vn = bn/yn1 for Hn to be maximized; or when bn P yn1 for Hn tomeet target;Vn = yn1/bn for Hn to be minimized; or when bn < yn1 for Hn tomeet target.

Or in a uniform manner:

Vn ¼maxfyn1=bng ¼minfyn1=bng ð6Þ

Step 9. Obtain final technical ratings of the HOWs: Those HOWswith higher initial technical ratings (tn’s), higher technical compet-itive priorities (zn’s) and higher improvement ratios (vn’s) indicateworking focuses and market opportunities for the producing com-pany. Final technical rating is a useful measure to reflect this pointwhich, with respect to Hn, can be computed for the company’sexcellence by integrating all these factors using a formula similarto (3) for computing the final importance ratings for the WHATs:

Sn ¼ Vn � tn � zn; n ¼ 1;2; . . . ;N ð7Þ

Thus we have a final technical rating vector on the HOWs,s ¼ ðs1; s2; . . . ; sNÞ. HOWs with higher final technical ratings, imply-ing greater importance for the company’s excellence to be success-ful in the competitive markets.

The above quantitative descriptions of our proposed HOE modelare summarized in Table 1.

In the next section, we will demonstrate step by step the con-cepts and operations of the model through an easy-to-understandautomotive Iranian company. To make our HOE model fully oper-able, we will use fuzzy method to handle the vagueness of people’slinguistic assessments and entropy method to derive competitivepriority ratings and also group analytic hierarchy process (GAHP)with both crisp and fuzzy approaches for determining the relativeimportance rating of excellence criteria or WHATs. A brief intro-duction to these three methods is given in the Appendix.

3.3. Population and data

Total population of this research was selected from the expertsof an automotive company as our case in this research. The totalpopulation was of 30 experts, and we obtained 30 valid responses.And data were collected from a self-constructed questioner.


3.4. Measurement

Data were collected from the experts and from all part of thatcompany through a questionnaire. Following the QFD methodol-ogy, a questionnaire covering aspects of each steps of that method-ology. The questionnaire method consists of the elaboration of abasic survey for QFD methodology in EFQM domain. Thus, all ofthe steps of QFD methodology assessed through several questions,which are evaluated on a scale (for instance, from 1 to 9 points).This approach enables the evaluation of the organization’s commit-ment towards each criterion and management tools and relation-ships between management tools and enablers criteria to becarried out and provides a score that quantifies the consistency be-tween organizational needs and QFD model and the EFQM excel-lence model. Consequently, questionnaires are useful to generatea quality pattern and to identify discrepancies in the organizationalneeds for excellence results.

After an exhaustive examination of a QFD methodology, thequestionnaire was operationalised through four parts. These fourparts were grouped into separately aggregated scales correspond-ing to each part. Cronbach’s alpha ranged between 0.89 and 0.94,indicating that the items deal with the same underlying construct.The managers assessed the items using a five-point Likert scaleranging from 1 (very low performance, very weak relation or abso-lutely not important) to 9 (very high performance, very strong rela-tion, or absolutely important). The validity of questionnaire waschecked by some experts that they were justified the validity ofthe questionnaire, so the validity of this research is contentvalidity.

3.5. Statistical procedure

In this research, we propose an alternative method for estimat-ing the relationships between the enablers and the managementtools with a QFD Model, based on the crisp and fuzzy approaches.So for testing the hypothesis of this research, we used spearmancorrelation coefficient, as this methodology implies the adoptionof a global approach in the study of the EFQM Excellence Model.Spearman correlation coefficient analysis is a statistical multivari-ate technique that summarizes the relations between two sets ofvariables. If our data are not normally distributed or have orderedcategories, we can choose Spearman, which measure the associa-tion between rank orders. Correlation coefficients range in valuefrom �1 (a perfect negative relationship) and +1 (a perfect positiverelationship). A value of 0 indicates no linear relationship. Wheninterpreting your results, be careful not to draw any cause-and-effect conclusions due to a significant correlation. Althoughspearman correlation coefficient can be conducted using standardstatistical software (e.g. SPSS), in this paper we use spss.13.

4. Results and discussion

In this section the questions of this research will answer andalso hypothesizes of this research will be test step by step withHOE model. Complete QFD examples to fully illustrate the proce-dure of QFD do not appear frequently in the literature, but they

Table 2Relative importance of each criterion by a GAHP method with crisp numbers.

are helpful for practitioners to follow. Here we present an automo-tive Iranian company example to illustrate the concepts and com-putations in our proposed HOE model in details. An automotiveIranian company, called company C1, wishes to make an improve-ment level of it’s excellence in response to the competition of othercompetitors in the same district. HOE technique can help C1 makethe appropriate decision resulting in better improvement.

The basic idea is (i) to understand what are organization needsfor excellence (such as EFQM enabler criteria like leadership, strat-egy, etc.) and then to identify the important ones through criteriaanalysis, and (ii) to associate the organization’s needs for excel-lence with appropriate technical attitudes or solutions (such man-agement tools like strategic management, productionmanagement, etc.) and then to find the important ones throughtechnical analyses. In what follows we will build the HOE modelfor this example step by step according to the qualitative andquantitative descriptions in Section 3.

Question 1: What are the effective criteria on setting EFQMexcellence model in an organization?

Step 1: At first the company must determine the experts to re-veal their various perceptions about the questions. Here, for illus-tration purpose, thirty experts of the company from all part of thecompany are selected to help conduct the HOQ analysis (i.e.,K = 30). By a complete literature review about the effective criteriaon setting EFQM mode, five enabler criteria of EFQM model asorganization’s needs for excellence (ONE) or WHATs are found(i.e., M = 5). They are: ‘‘leadership’’, ‘‘strategy’’, ‘‘employee’’, ‘‘shar-ing & resources’’ and ‘‘processes’’.

Question 2: What are the ranking of effective criteria on settingEFQM excellence model in an organization (the research case)?

Step 2: The five WHATs can hardly be of same importance to theorganization. So by use of pair wises comparison between each towcriteria, the thirty experts are asked to reveal their perceptions onthe relative importance of the five WHATs using the five linguisticterms in scale, so each expert should answer ten questions in pairwise form and then by use of group analytic hierarchy process(GAHP), the relative importance of each criteria were calculated.Table 2 shows the final pair wises comparison of all the aggrega-tion of experts and the relative importance of each enabler criteriawith crisp numbers. Table 3(a) shows the final fuzzy pair wises andTable 3(b) shows the fuzzy relative importance of each criterionwith fuzzy numbers which are the results of using a fuzzy analytichierarchy process (FAHP), as shown in Appendix. Suppose that ex-pert 1 rates the importance of W1 by comparison to W2 as ‘‘high’’.Using scale (2), these linguistic assessments of the WHATs’ relativeimportance can be converted to crisp numbers or symmetrical tri-angular fuzzy members (STFNs) according to practical need. Forexample, expert 1 considers W1 as having ‘‘high’’ importance thanW2, which can be represented by a crisp number 7 or an STFN [6, 8]according to scale (2) so we show this kind of fuzzy numbers asM = (l,m,u). for above example it can be represented by (6,7,8) asa STFN.

Table 3aFinal fuzzy comparisons matrix of criterion.

Table 3bFuzzy relative importance of each criterion by a FGAHP method.

Criteria Fuzzy relative importance Defuzzified relative importance Relative importance (gfw)

L M U

W1 0.065588 0.094353 0.124516 0.0947 9.04W2 0.079553 0.115859 0.166 0.11932 11.39W3 0.105682 0.156506 0.220246 0.15974 15.25W4 0.195478 0.311328 0.501182 0.32983 31.48W5 0.202162 0.321954 0.530163 0.34406 32.84

Table 4Final excellence competitive analysis matrix X = [xml]5�7.

WHATS C1 C2 C3 C4 C5 C6 C7 em

W1 6.46 5.46 4.93 5.56 5.66 4.16 6.90 0.200003W2 5.56 5.40 4.53 5.26 5.36 3.83 6.70 0.199318W3 5.33 5.46 4.96 5.23 5.73 4.06 6.53 0.200302W4 5.93 5.20 4.66 5.03 5.20 3.86 6.53 0.199979W5 5.53 5.06 4.80 5.23 5.46 4.60 6.83 0.200399


The AHP methodology of Satty (1980) provides a consistency ra-tion to measure any inconsistency whit in the judgments in eachcomparison matrix as well as for the entire hierarchy. The rationcan be use to indicate whether or not the largest can be arrangedin an appropriate order of ranking and how consistent are the pairwise comparison matrixes. If the calculated consistency rate of a fi-nal comparison matrix is less than 0.1, then the consistency of thepairwise judgment can be thought as being acceptable. Otherwisethe judgments expressed by the experts are considered to beinconsistent, and the decision makers have to repeat the pairwisecomparisons. In this research after calculating the consistency rateof the entire comparison matrix and also the final comparison ma-trix that made by a geometric mean method, it was found they areall less than 0.1. Therefore, the consistency of the judgment in allthe comparison matrices is acceptable.

Step 3: This step is for company C1 to identify competitors andconduct excellence competitive analysis. In the district’s automo-tive market, company C1 has six main competitors, called companyC2, C3, C4, C5, C6 and C7, each of which makes a similar type of prod-ucts. In order to understand the automotive market and its relativeposition in the market, and to finally find out the priorities for fur-ther improvement, company C1 asks all the experts to rate the rel-ative performance of its own company and the six competitors’similar products in terms of the five WHATs using scale (2). Forexample, expert 1 rates the performance of C2’s performances onW3 as ‘‘neutral’’ using scale (2), which corresponds to a crisp num-ber of 5, i.e., X321 = 5. We will not consider using STFNs to representperformance assessments since it is too complex to incorporateSTFNs into the following entropy computations.

The final excellence competitive analysis of the thirty experts’assessments are shown in Table 4, where, according to the thirty’assessments of the relative performance of the seven company’similar products in terms of the 5 WHATs, a excellence comparisonmatrix X = [Xmn]5�7 can be obtained by averaging the customers’assessments.

Applying the entropy method as illustrated in the Appendix, wecan obtain company C1’s competitive priority ratings on the 5 orga-nization’s needs for excellence based on the above excellence com-parison matrix X. For example, the ‘‘leadership’’ of W1 on the sevencompanies’ excellence performance is composed of the seven com-panies’ performance ratings on W1: (6.46, 5.46, 4.93, 5.56, 5.66,4.16, 6.90), which is the first row of matrix X. Then we can computethe total score of W1: x1 = x11 + x12, . . ., + x16 + x17 = 6.46+ 5.46 +,. . ., + 4.16 + 6.90 = 39.17, and obtain the ‘‘probability leadership’’of W1:

P11 = X11/X1 = 6.46/39.17 = 0.165106P12 = X12/X1 = 5.46/39.17 = 0.139574P13 = X13/X1 = 4.93/39.17 = 0.125957P14 = X14/X1 = 5.56/39.17 = 0.142128P15 = X15/X1 = 5.66/39.17 = 0.144681P16 = X16/X1 = 4.16/39.17 = 0.106383P17 = X17/X1 = 6.90/39.17 = 0.176170

The entropy of W1 is then computed using (A.20) as:

EðW1Þ ¼ �UX7

l¼1

Pl1 lnðPl1Þ

¼ ½0:165106 ln 0:165106þ � � � þ 0:17617 ln 0:17617�¼ 1:93442

We can obtain in the same way the entropy for each of the 5 orga-nizations’ needs for excellence as:

ðEðW1Þ; EðW2Þ; . . . ; EðW5ÞÞ¼ ð1:93444;1:92782;1:93734;1:93421;1:9383Þ

Finally, according to (A.21) we can obtain company C1’s competitivepriority ratings on the Wj’s:

e ¼ ðe1; e2; . . . ; e5Þ¼ ð0:200003;0:199318;0:200302;0:199979;0:200399Þ

where, for example,

e1 ¼ EðW1Þ=X

EðWmÞ ¼ 1:93444=ð1:93444þ � � � þ 1:9383Þ

¼ 1:3137=9:05144 ¼ 0:200003

This set of competitive priority ratings are shown in the last columnof Table 4 from which we know that W5 is of the highest competi-tive priority for the company, followed by W3; W4 and W2.

Table 5Improvement ratio of WHATs.

WHATs am U = GOAL/Xm1

W1 8 1.23711W2 7 1.25749W3 7 1.31250W4 7 1.17978W5 7 1.26506


Based on the resources available and the relative performance ofthe seven company on the 5 WHATs, company C1 can set improvinggoals on each WHAT to better satisfy the organizations’ needs forexcellence. After various considerations, company C1 decides the fol-lowing performance goals on the WHATs using scale (2):

a ¼ ða1; a2; a3; a4; a5Þ ¼ ð8;7;7;7;7Þ

This set of goals is shown in Table 5. It is noted that all goal perfor-mance levels are higher than C1’s current performance levels repre-sented by the first column of excellence comparison matrix X. If C1’sperformance on a WHAT is poorer or much poorer than the perfor-mance of most of its competitors, then the goal level is set to bemuch higher than its current level to be of competitiveness. Other-wise, if C1’s performance on a WHAT is better than the performanceof most of its competitors, then the goal level is only set to beslightly higher than its current level which is enough for C1 to keepand enhance its established competitiveness. We do not considersetting goals in STFN form either, since these results in some com-putational and explanatory difficulties.

According to company C1’s current and goal performance levelson the five WHATs, its improvement ratios with respect to theorganizations’ needs for excellence can be easily computed accord-ing to the formula um = am/xm1:

u ¼ ðu1;u2;u3;u4;u5Þ ¼ ð1:2371;1:2575;1:3125;1:1798;1:2651Þ

Step 4: According to each WHAT’s relative importance rating, com-petitive priority rating and improvement ratio, company C1 couldnow reach the final importance rating of the WHAT using (3). Incase that the relative importance ratings are crisp numbers, the fi-nal importance ratings are also given as the following crispnumbers:

f ¼ ðf1; f2; f3; f4; f5Þ ¼ ð0:0286;0:02807;0:0369;0:0680;0:0870Þ

Here, for example, the final importance rating of W1 in crisp form, f1,is computed by (3) as:

f1 ¼ u1 � g1 � e1 ¼ 1:23711� 0:1157� 0:200003 ¼ 0:0286

From f we can finally rank the importance of the five WHATs in thefollowing order:

W5 > W4 > W3 > W1 > W2

where ‘‘>’’ means ‘‘more important than’’.If relative importance ratings are STFNs, final importance rat-

ings are also given as STFNs:

f f ¼ ðf f1 ; f

f2 ; f

f3 ; f

f4 ; f

f5 Þ

¼ ð½0:016228;0:030808�; ½0:019939;0:041606�;½0:027784;0:0579�; ½0:046119;0:118244�;½0:051251;0:134405�Þ

Here, for example, the final importance rating of W1 in STFN form,f f

1, is computed by (3) and the scalar multiplication rule of STFNsas:

f f1 ¼ u1 � gf

1 � e1 ¼ 1:2371� ½0:065;0:124� � 0:200003

¼ ½0:016228;0:03808�

Applied to a triangular fuzzy number FN = (fL, fM, fU), for defuzzifica-tion of a STFN, the Facchinetti, Ghiselli Ricci, and Muzioli (1998) ap-proach produces a score identified by the value:

Score ¼ ðfL þ 2f M þ fUÞ=4 ð8Þ

f ¼ ðf1;2; f3; f4; f5Þ¼ ð0:023432;0:029906;0:041994;0:077817;0:087224Þ

So the final importance of WHATs is as:

W5 > W4 > W3 > W2 > W1

Both sets of ratings indicate that W5 is the most important WHAT,followed by W4 and W3. But the crisp approach show that W1 ismore important than W2 but in fuzzy approach W2 is more impor-tant than W1.

These final importance ratings of the WHATs, expressed asboth crisp numbers and STFNs, are shown in the second columnof Table 6.

Hypothesize 1: The ranking of effective criteria on settingEFQM model in an organization (the research case) are the samein crisp and fuzzy approaches.

In order to be comparable, the crisp and fuzzy final importanceratings are tested by spearman correlation coefficient. According tothe Table 6, with percentage results of crisp and fuzzy ranking, thespearman coefficient correlation for these tow type of data is 0.9and there is a very strong positive correlation between fuzzy andcrisp importance ranking. So the first hypothesis of this researchthat maintains: ‘‘The ranking results of effective criteria on settingEFQM model in an organization (the research case) are the same incrisp and fuzzy approaches.’’ were supported.

Question 3: what are the effective management tools on settingEFQM excellence model in an organization?

Step 5: Now it is time to convert organizations’ needs for excel-lence (ONE) into technical attributes specifications. After carefulconsiderations and literature review (Darrell, 2007; EFQM, 2000;Ignacio, 2005), 15 technical attributes for excellence (HOWs) thatrelate to and can help realize the five WHATs are proposed as:

H1 = inventory managementH2 = total quality managementH3 = human resources managementH4 = knowledge managementH5 = technology managementH6 = information managementH7 = energy managementH8 = project managementH9 = financial managementH10 = change managementH11 = customers relationship managementH12 = supply chain managementH13 = business process managementH14 = strategic managementH15 = production management

Question 4: what are the ranking of the effective managementtools on setting EFQM excellence model in an organization (theresearch case)?

Step 6: Then the experts begin to establish the relationships be-tween the HOWs and the WHATs, or to examine to what extent eachHOW is related to each WHAT. This step is usually done simulta-neously with Step 5 since in the process of generating HOWs, each

Table 6Normalization and determine the percentages of the five WHATs.

WHATs Crisp (fM) Fuzzy (f fM)

Crisp weights Nor. Per. % Fuzzy weights Defuzzi. Nor. Per. %

W1 0.0286 0.115058 11.50 [0.016,0.030] 0.023432 0.089994 9.00W2 0.02807 0.112926 11.30 [0.020,0.042] 0.029906 0.114858 11.47W3 0.0369 0.148449 14.84 [0.028,0.058] 0.041994 0.161284 16.13W4 0.0680 0.273565 27.36 [0.046,0.118] 0.077817 0.298867 29.90W5 0.0870 0.350002 35.00 [0.051,0.134] 0.087224 0.334996 33.50

Table 7Final relationship matrix between WHATs and HOWs with both crisp and STFNs.

Final matrix HOWs

WHATs L Inventory management U L Total quality management U L Human resource management U

W1 4.1 5.1 6.1 5.5 6.5 7.5 6.2 7.2 8.2W2 3.7 4.7 5.7 4.4 5.4 6.4 5.6 6.6 7.6W3 4.1 5.1 6.1 4.5 5.5 6.5 4.8 5.8 6.8W4 5.0 6.0 7.0 3.7 4.7 5.7 4.0 5.0 6.0W5 4.7 5.7 6.7 4.7 5.7 6.7 4.2 5.2 6.2

L Knowledge management U L Technology management U L Information management UW1 4.8 5.8 6.8 4.5 5.5 6.5 5.1 6.1 7.1W2 4.6 5.6 6.6 4.1 5.1 6.1 5.1 6.1 7.1W3 4.1 5.1 6.1 5.1 6.1 7.1 4.4 5.4 6.4W4 4.2 5.2 6.2 5.3 6.3 7.3 4.8 5.8 6.8W5 4.9 5.9 6.9 5.0 6.0 7.0 4.4 5.4 6.4

L Energy management U L Project management U L Financial management UW1 4.5 5.5 6.5 3.9 4.9 5.9 5.3 6.3 7.3W2 4.6 5.6 6.6 4.0 5.0 6.0 4.5 5.5 6.5W3 3.4 4.4 5.4 4.0 5.0 6.0 4.8 5.8 6.8W4 4.2 5.2 6.2 4.0 5.0 6.0 5.0 6.1 7.1W5 4.0 5.0 6.0 3.8 4.8 5.8 5.0 6.0 7

L Change management U L Customer relationship management U L Supply chain management UW1 4.6 5.6 6.6 4.5 5.5 6.5 4.4 5.4 6.4W2 4.7 5.7 6.7 4.2 5.2 6.2 3.6 4.6 5.6W3 4.8 5.8 6.8 5.1 6.1 7.1 4.3 5.3 6.3W4 4.6 5.6 6.6 4.9 5.9 6.9 5.0 6.0 7.0W5 5.0 6.0 7.0 4.1 5.1 6.1 4.0 5.8 6.8

L Business process management U L Strategic management U L Production management UW1 5.1 6.1 7.1 4.7 5.7 6.7 4.0 5.0 6.0W2 4.3 5.3 6.3 3. 4.9 5.9 4.2 5.2 6.2W3 5.1 6.1 7.1 5.5 6.5 7.5 5.2 6.2 7.2W4 5.5 6.5 7.5 4.3 5.3 6.3 6.0 7.0 8.0W5 5.0 6.0 7.0 4.8 5.8 6.8 6.0 7.0 8.0


HOW’s relationships with the WHATs are always examined once theHOW is considered. The relationships between the HOWs and theWHATs are determined by technical analysis and empirical judg-ment, and usually may not be precise. So it is quite appropriate touse STFNs to represent this kind of relationships. For each HOWwith respect to each WHAT, the experts determine the relationshipfirst in linguistic term using scale (4) and then convert this relation-ship into corresponding crisp number and STFN, for example, theexpert consider the relationship between H1 and W1 as ‘‘verystrong’’ that corresponds to a crisp number of 9 and an STFN [8, 10].

The full matrix of these relationships, both in crisp numbers andSTFNs, are shown in Table 7 where be obtained by averaging the ex-pert’ assessments about the relationship between WHATs andHOWs.

Step 7: According to the WHATs’ final importance ratings andthe relationship values between the HOWs and the WHATs, theHOWs’ initial technical ratings can be computed usually throughthe simple additive weighting (SAW) formula (5). When crispnumbers are used, the initial technical ratings are given as

t ¼ ðt1; . . . ; t15Þ¼ ð1:38;1:35;1:40;1:38;1:48;1:42;1:27;1:23;1:49;1:44;

1:39;1:40;1:51;1:41;1:61Þ

Here, for example, crisp initial technical rating of H1, t1, is computedas the weighted average over H1’s crisp relationship values with thefive WHATs, r11, r21, . . ., r51, which correspond to the crisp part of therelationship matrix that is bolded in Table 7, and the weights arethe crisp final importance ratings of the five WHATs, f1, f2, . . ., f5, i.e.,

t1 ¼X5

m¼1

fm � rm1

¼ 0:0286� 5:1þ 0:02807� 4:7þ 0:0369� 5:1þ 0:068� 6:0þ 0:087� 5:7 ¼ 1:38

From these crisp initial technical ratings, the technical measures(HOWs) can be ranked in the following order:

H15 > H13 > H9 > H5 > H10 > H6 > H14 > H12 > H11 > H3

> H1 > H4 > H2 > H7 > H8 ð9Þ

If fuzzy numbers of the relationship matrix are used, the fuzzy ini-tial technical ratings are also given as STFNs:

tf ¼ ð½0:73;2:52�; ½0:72;2:46�; ½0:75;2:52�; ½0:73;2:51�; ½0:8;2:67�;½0:76;2:57�; ½0:66;2:33�; ½0:64;2:28�; ½0:8;2:68�; ½0:77;2:60�;½0:74;2:52�; ½0:74;2:55�; ½0:82;2:72�; ½0:76;2:56�; ½0:88;2:89�Þ

Table 8The crisp and fuzzy initial technical rating of HOWs.

HOWs

L Inventory management U L Total quality management U L Human resource management U

tf 1.38 1.35 1.4tf 0.73 2.52 0.72 2.46 0.75 2.52

L Knowledge management U L Technology management U L Information management U

tf 1.38 1.48 1.42tf 0.73 2.5 0.79 2.6 0.76 2.56

L Energy management U L Project management L Financial management U

tf 1.26 1.23 1.49tf 0.66 2.33 0.64 2.2 0.80 2.68

L Change management U L Customer relationship management U L Supply chain management U

tf 1.43 1.39 1.40tf 0.77 2.60 0.74 2.52 0.74 2.55

L Business process management U L Strategic management U L Production management U

tf 1.51 1.41 1.61tf 0.82 2.72 0.76 2.56 0.88 2.89

Table 9Final technical competitive analysis matrix Y = [ynl]15�7.

HOWs C1 C2 C3 C4 C5 C6 C7

Strategic management 5.56 5.00 4.43 4.96 5.40 4.50 6.43Business process management 6.30 5.06 3.86 5.20 5.33 4.66 6.20Supply chain management 6.53 5.30 5.03 5.33 5.56 4.76 6.66Customer relationship management 6.13 5.46 5.10 5.20 5.60 4.33 6.70Change management 5.20 4.73 4.76 4.60 4.66 4.33 6.36Financial management 6.03 4.43 4.86 5.23 5.33 4.73 6.66Project management 5.63 5.03 4.80 4.83 5.10 5.00 6.56Energy management 5.16 4.4 4.70 4.86 5.3 4.63 6.13Information management 5.13 4.86 4.66 5.13 4.96 4.6 6.9Technology management 4.80 4.70 4.63 5.00 4.93 4.4 6.46Knowledge management 5.03 5.10 4.50 5.03 4.93 4.86 6.53Human resource management 5.76 4.83 4.53 5.33 5.50 4.76 6.60Total quality management 5.70 5.16 4.23 4.86 5.23 4.56 6.53Inventory management 5.36 4.83 4.96 4.90 5.16 4.93 6.23Production management 6.46 5.23 5.03 5.26 5.23 5.13 7.03


Here, for example, the initial technical rating of H1 in STFN form, tf1,

is computed as the weighted average over H1’s STFN form relation-ship values with the five WHATs, rf

11,rf21; . . . ; rf

51, which correspondto the first column of the STFN form relationship matrix Rf, andthe weights are the final importance ratings of the five WHATs inSTFN form, f f

1 ; ff2 . . . f f

5 , i.e.,

tf1 ¼

X5

m¼1

f fm � rf

m1

¼ ½0:016;0:0308� � ½4:1;6:1� þ � � � þ ½0:05; 0:134� � ½4:7;6:7�¼ ½0:73;2:52�

According to the principle in the Appendix, these fuzzy ratings havethe following ranking order for the HOWs’ initial importance:

H15 > H13 > H9 > H5 > H10 > H6 > H14 > H12 > H11 > H3

> H1 > H4 > H2 > H7 > H8 ð10Þ

It is noticed from (9) and (10) that the crisp and fuzzy ratings exhibitthe same ranking order. Both sets of ratings indicate that H15 is of thehighest initial importance, followed by H13, H9 and H5. The crisp andfuzzy initial technical ratings of the 15 HOWs are shown in Table 8.

Step 8: Now turn to technical competitive analysis which is tofind and establish competitive advantages or to further enhancethe existing advantages for company C1, through comparing allthe company’ similar products in terms of their technical perfor-mance on the 15 identified HOWs. Although it is always not easyto acquire the technical performance levels of competitors’ perfor-

mance on the HOWs, company C1 must try all the means to obtainthis valuable information in order to know its technical strengthsand weaknesses and hence to improve or enhance its competitive-ness. Through a lot of efforts company C1 obtains all the technicalparameters of its own and its competitors in terms of the 15HOWs. This information forms a technical comparison matrixY = [ynl]15�7 as shown in Table 9.

Applying entropy method to Y in the same manner as in excel-lence competitive analysis (Step 3), technical competitive priorityratings can be obtained for company C1 on the 15 HOWs:

z ¼ ðz1; z2; . . . ; z14; z15Þ¼ ð0:071431;0:071285;0:071443; . . . ;0:07157;0:071409Þ

From these ratings which we know that H9; H15 and H5 are of thehighest competitive priorities. According to the technical perfor-mance of its own and the other six competitors company in termsof the 15 HOWs, company C1 could set technical performance goalon each of the HOWs for itself.

To better fulfill the customer needs. These goals should bedetermined both competitively and realistically. Company C1’s rel-evant experts agree with the following performance goals on theHOWs for further improvement:

b ¼ ðb1; b2; . . . ; b14; b15Þ ¼ ð7;8;8;8;7;8;7;7;7;6;7;7;7;7;8Þ

From these goal (bn) and current (yn1) technical performance levels,improvement ratios for company C1 to be competitive in terms ofthe HOWs can be easily computed using (6) as:

:


v ¼ ðv1; v2; . . . ; v14;v15Þ¼ ð1:25749;1:26984; . . . ;1:30435;1:23711Þ

Step 9: This is the last step of our proposed HOE model. Integrat-ing the initial technical ratings, technical competitive priority rat-ings and improvement ratios of the HOWs, final technical ratingscan be computed by (7). If initial technical ratings are crisp num-bers, the final technical ratings are also crisp numbers and givenas:

s ¼ ðs1; s2; . . . ; s14; s15Þ ¼ ð0:12411;0:12247; . . . ; 0:13315;0:13205Þ

Here, for example, the final technical rating of H1 in crisp form, s1, iscomputed by (7) as:

s1 ¼ v1 � t1 � z1 ¼ 1:25749� 1:38� 0:071431 ¼ 0:12411

From s we can rank the final technical importance of the nine HOWsin the following order:

H9 > H15 > H5 > H11 > H6 > H13 > H14 > H10 > H4 > H1

> H3 > H2 > H12 > H8 > H7 ð11Þ

This final technical importance order differs from the initial techni-cal importance order (9) in two aspects: (i) H15 is of higher initialtechnical importance but lower final technical importance thanH9; and (ii) H8 is of lower initial technical importance but high finaltechnical importance than H7. Since technical competitive priorityratings (zn’s) do not vary too much, these two differences are mainly

Table 10Crisp and fuzzy final technical ratings of the 15 HOWs.

HOWs Final technical ratings Scaled final technical ratings

Crisp (sn) Fuzzy (sfn) Crisp (sn) Fuzzy (sf

n)

H1 0.124113 [0.066, 0.227] 0.854326 [0.252, 0.868]H2 0.122475 [0.065, 0.223] 0.843047 [0.249, 0.853]H3 0.122697 [0.066, 0.220] 0.844581 [0.252, 0.846]H4 0.128393 [0.068, 0.233] 0.883786 [0.261, 0.897]H5 0.142687 [0.077, 0.257] 0.982174 [0.294, 0.987]H6 0.134406 [0.072, 0.243] 0.925178 [0.276, 0.930]H7 0.112604 [0.059, 0.207] 0.77510 [0.225, 0.794]H8 0.119689 [0.062, 0.221] 0.82387 [0.238, 0.847]H9 0.145276 [0.079, 0.261] 1.00000 [0.301, 1.000]H10 0.128473 [0.069, 0.232] 0.884335 [0.264, 0.889]H11 0.138175 [0.074, 0.251] 0.951116 [0.282, 0.959]H12 0.121494 [0.065, 0.221] 0.836298 [0.247, 0.847]H13 0.133152 [0.072, 0.239] 0.916544 [0.276, 0.915]H14 0.132053 [0.071, 0.239] 0.908977 [0.271, 0.914]H15 0.142942 [0.078, 0.256] 0.983933 [0.299, 0.980]

Table 11Normalization and determine the percentages of the 15 HOWs.

HOWs Crisp (sn)

Crisp weights Nor. Per. %

H1 0.124113 0.063693 6.36926H2 0.122475 0.062852 6.28517H3 0.122697 0.062966 6.29661H4 0.128393 0.065889 6.58889H5 0.142687 0.073224 7.32241H6 0.134406 0.068975 6.89749H7 0.112604 0.057786 5.77861H8 0.119689 0.061422 6.1422H9 0.145276 0.074553 7.45531H10 0.128473 0.06593 6.59299H11 0.138175 0.070909 7.09086H12 0.121494 0.062349 6.23485H13 0.133152 0.068331 6.83312H14 0.132053 0.067767 6.7767H15 0.142942 0.073355 7.33552

caused by the setting of performance goals (bn’s) or improvementratios (vn’s): (i) H15’s improvement ratio (1.23711) is lower thanH9’s (1.3636), and (ii) H8’s improvement ratio (1.3548) is higherthan H7’s (1.2426).

If initial technical ratings are STFNs, then the final technical rat-ings are also given as STFNs:

sf ¼ ðsf1; . . . ; sf

15Þ¼ ð½0:066;0:227�; ½0:065;0:223�; . . . ; ½0:071;0:239�; ½0:078;0:256�Þ

Here, for example, the final technical rating of H1 in STFN form, sf1,

is computed by (7) and the arithmetic of STFNs as:

sf1 ¼ v1 � tf

1 � z1 ¼ 1:25749 � ½0:73; 2:52� � 0:071431

¼ ½0:066; 0:227�

These fuzzy ratings produce the following ranking order for theHOWs’ final importance:

H9 > H15 > H5 > H11 > H6 > H13 > H14 > H10 > H4

> H1 > H3 > H2 > H12 > H8 > H7: ð12Þ

It is noticed from (11) and (12) that the crisp and fuzzy ratings showan almost identical ranking order for the HOWs’ final technicalimportance. Both sets of ratings indicate that H9 is the most impor-tant HOW, followed by H15 and then by H11 and H6, and that H7 isthe least important HOW, preceded by H38 and H12.

These crisp and fuzzy final technical ratings of the HOWs areshown in Table 10. In order to be comparable, they are both scaledto have maximum rating or upper limit of unity, which are alsoshown in Table 10. From these scaled ratings we can see again that,although the crisp and fuzzy ratings exhibit an identical trend,crisp ratings always tend to be close to the upper limits of the cor-responding fuzzy ratings. This shows that fuzzy ratings are morerepresentative of the possible variations of the HOWs’ technicalimportance, which would make the technical improvement moreflexible and the design process more feasible.

The above nine steps complete the HOE process for improvingthe company’s improvement trends on excellence. The corre-sponding tables of results, after appropriate arrangement, canform an HOE like Fig. 2 which links organization needs to tech-nical considerations and exhibits all the relevant elements andtheir relationships. As a result of this HOE model, it is concludedthat H7 could be deleted from further consideration (in QFD’ssecond phase, parts deployment) to save technical efforts with-out decreasing organization satisfaction. If resource or budgetconsiderations require to further cut down the number of HOWs,

Fuzzy (sfn)

Fuzzy weights Defuzzi. Nor. Per. %

[0.066,0.227] 0.135156 0.063825 6.38254[0.065,0.223] 0.133169 0.062887 6.288714[0.066,0.220] 0.13305 0.062831 6.283073[0.068,0.233] 0.139687 0.065965 6.59649[0.077,0.257] 0.154875 0.073137 7.313718[0.072,0.243] 0.145948 0.068922 6.892164[0.059,0.207] 0.122847 0.058013 5.801264[0.062,0.221] 0.130701 0.061721 6.172143[0.079,0.261] 0.157568 0.074409 7.440888[0.069,0.232] 0.139532 0.065892 6.589179[0.074,0.251] 0.150141 0.070902 7.090178[0.065,0.221] 0.132208 0.062433 6.243329[0.072,0.239] 0.144356 0.06817 6.816961[0.071,0.239] 0.143414 0.067725 6.772494[0.078,0.256] 0.154942 0.073169 7.316866


H8; H12 and H2 form a good deleting order that will not signifi-cantly influence the fulfillment of the organization needs. Andalso, according to the Table 10, by use of these crisp and fuzzyimportances, the importance weighting of each management toolcan be computed. In this way, first of all, the crisp importancevalues of management tools should be normalized, and thenthe percentages of them should be calculated. But for the fuzzyimportance values, at first, the defuzzification of the fuzzy valuesof management tools should be determined, for this step hasdone by the Facchinetti et al. (1998) approach, and then the nor-malization and determine the percentage of them, should bedone (see Table 11).

Hypothesize 2: The ranking results of effective managementtools on setting EFQM excellence model in an organization(the research case) are the same in crisp and fuzzy approaches.

In order to be comparable, the crisp and fuzzy final importanceratings are tested by spearman correlation coefficient. According tothe Table 11, with percentage results of crisp and fuzzy ranking,the spearman coefficient correlation for these tow type of data is0.993 and there is a very strong positive correlation between fuzzyand crisp importance ranking. So the first hypothesis of this re-search that maintains: ‘‘The ranking results of effective manage-ment tools on setting EFQM excellence model in an organization(the research case) are the same in crisp and fuzzy approaches.’’,were supported.

5. Conclusions

Using the useful management tools that are relevant to theorganization’s needs for excellence has become so important. Bychoosing and applying the best management tools among too

Leadership (9.00)

Strategy (11.47)

Employee (16.13)

Resources (29.90)

Processes (33.50)

Fig. 4. The basis for programming and organiz

many management tools, companies can improve their perfor-mances and then increase customer satisfaction and gain marketshares. But for the organizations, that adopted excellence modelssuch as EFQM, to improve their performances, selection and choos-ing these management tools has been a big challenge in today’s dy-namic environment. This paper presents a systematic andoperational approach to HOE to help resolve this problem. Thisstudy has addressed the applicability of QFD in the organizationalexcellence context. More specifically, an original methodology hasbeen proposed and adopted to rank viable EFQM excellence criteriaand the management tools a firm can undertake to improve excel-lence performances.

The methodology developed could be rightly considered as auseful tool for selecting the most efficient and effective manage-ment tools leverages to reach organizational excellence. We pro-pose a 9-step HOE model, which is basically a QFD model, tounify the HOE process and a few 9-point scales to unify the mea-surements in HOE to avoid arbitrariness and incomparability.

We especially address the various ‘‘voices’’ in the HOE processand suggest the use of symmetrical triangular fuzzy numbers(STFNs) to reflect the vagueness in expert’s linguistic assessments.Furthermore, we employ the quantitative entropy method to con-duct competitive analysis and derive competitive priority ratings.All information required, computations involved and feasiblemethods are clearly indicated to give an applicable frameworkfor practitioners to perform HOE analysis without confusions anddifficulties. To fully illustrate our proposed HOE model, we presentan automotive company example that involves five organizationalneeds for excellence (EFQM enabler criteria), 15 technical attri-butes for excellence (management tools) and seven competitorcompanies.

In a similar manner, the weighted importance of managementtools allows the firm to identify the key factors of intervention inorder to improve the perceived excellence. As an example, pro-

Inventory management (6.38)

Total quality management (6.29)

Human resources management (6.28)

Knowledge management (6.60)

Technology management (7.31)

Information management (6.89)

Energy management (5.80)

Project management (6.17)

Financial management (7.44)

Change management (6.59)

Customer relationship management (7.09)

Supply chain management (6.24)

Business process management (6.82)

Strategic management (6.77)

Production management (7.32)

ational resources allocation for excellence.


cesses emerges in step 4 as the most important factor from experts’point of view, and it should be considered as the key excellence cri-terion to improve the performance of the organization. In order toassess and rank viable management tools, in the approach pro-posed we have introduced entropy method, which considers thecompetition of implementation for each ‘‘what’’ and ‘‘how’’. Theentropy can be directly adopted as a synthesis parameter to selectthe most suitable EFQM enabler criteria and management toolsthat have the most competitive importance to implement. Accord-ing to step 3 and step 8, it is considered that WHATs and HOWs,both of them have the same competitive importance. Since per-sonal judgments are required when building the HOE, fuzzy logichas been adopted as a useful tool. Through fuzzy logic linguisticjudgments an expert gives to weights, relationships and correla-tions have been appropriately translated into triangular a fuzzynumber. Moreover, fuzzy logic has allowed to cope well withuncertainties and incomplete understanding of the relationshipsbetween ‘‘WHATs’’ and between ‘‘HOWs’’ and ‘‘WHATs’’. In addi-tion, fuzzy logic becomes fundamental to dealing with severalparameters that seem difficult to express in a quantitative mea-sure. As an example, detailed information about relationships be-tween management tools and EFQM excellence criteria areusually not available, while linguistic judgments on them can beeasily obtained.

By use of the fuzzy importance percents ranking of EFQM crite-ria and management tools, from Tables 6 and 11, the basis for pro-gramming and allocating of organization resources for theimproving of excellence performances, can provided. It is shownin Fig. 4. The methodology proposed does not deal with the practi-cal implementation of management tools. Future work may bethus directed to extend a similar QFD approach from a strategic le-vel to tactical and operational ones. Specially, future work can ex-tend sub-set of each management tools in to the other phases ofQFD approach.

Appendix A

A.1. Fuzzy methods

Fuzzy set theory was developed for solving problems inwhich descriptions of objects are subjective, vague and impre-cise, i.e., no boundaries for the objects can be well defined. LetX = {x} be a traditional set of objects, called the universe. A fuzzyset eE in X is characterized by a membership function leEðxÞ thatassociates each object in X with a membership value in theinterval [0, 1], indicating the degree of the object belonging toeE. A fuzzy number is a special fuzzy set when the universe Xis the real line R1 : �1 < x < +1. A symmetrical triangular fuzzynumber (STFN), denoted as eE ¼ ½0;1�, is a special fuzzy numberwith the following symmetrical triangular type of membershipfunction:

leEðxÞ ¼ 1� j x� ðc þ aÞ=2 j =½ðc � aÞ=2�; a � x � c ðA:1Þ

STFN is widely used in practice to represent a fuzzy set or concepteE = ‘‘approximately b’’ where b = (a + c)/2. For example, if an EFQMenabler criterion leadership is rated as having ‘‘very high’’ impor-tance by a decision maker, then traditionally we may assign leader-ship a number 9 using crisp scale. To capture the vagueness of thedecision maker’s subjective assessment, we can according to thesame scale assign leadership an STFN [8, 10] which means ‘‘approx-imately 9’’ and is represented by the following membershipfunction:

l½8;10�ðxÞ ¼ 1� j x� 9 j; 8 � x � 10: ðA:2Þ

This means that, for example, the membership value or ‘‘possibility’’that leadership is assigned a number 9 is l[8,10](9) = 1, the ‘‘possibil-ity’’ that leadership is assigned a number 8.5 or 9.5 is l[8,10](8.5) =0.5 or l[8,10](9.5) = 0.5. So assigning leadership a number 8.5 or9.5 is acceptable or ‘‘possible’’ to the degree of 50%. The basic arith-metic rules for STFNs are as follows:

Addition : ½a; b� þ ½c;d� ¼ ½aþ c; bþ d� ðA:3Þ

Subtraction : ½a; b� � ½c;d� ¼ ½a� c; b� d� ðA:4Þ

Scalar multiplication : k� ½a; b� ¼ ½ka; kb� k > 0 ðA:5Þ

Multiplication : ½a; b� � ½c; d� � ½ac; bd�; a � 0 c � 0 ðA:6Þ

Division : ½a; b� ½c;d� � ½a=c; b=d�; a � 0; c > 0 ðA:7Þ

For any two STFNs, eE1 = [a, b] and, eE2 = [c, d], if one interval is notstrictly contained by another then their ranking order can be easilyand intuitively determined. That is If d > b and c P a, or d P b and c > b, then eE2 > eE1, where ‘‘>’’

means ‘‘is more importance or preferred than’’. If a = c, b = d, then eE2 ¼ eE1

But if one interval is strictly contained by another, i.e., if d < b andc > a, or d > b and c < a, then the ranking problem becomes complexand many possibilities may occur. For more details about fuzzy settheory, STFNs and fuzzy ranking methods, see Zimmermann(1987).

A.2. Fuzzy AHP

To apply the process depending on this hierarchy, according tothe method of Chang’s (1996) extent analysis, each criterion is ta-ken and extent analysis for each criterion, gi; is performed on,respectively. Therefore, m extent analysis values for each criterioncan be obtained by using following notation (Kahraman, Cebeci,& Ruan, 2004):

M1gi;M

2gi;M

3gi; . . . ;Mm

gi

where gi is the goal set (i = 1, 2, 3, 4, 5, . . ., n) and all the Mjgi

(j = 1, 2, 3, 4, 5, . . ., m) are triangular fuzzy numbers (TFNs). Thesteps of Chang’s analysis can be given as in the following.

Step 1: The fuzzy synthetic extent value (Si) with respect to theith criterion is defined as Eq. (A.8)

Si ¼Xm

j�1

Mjgi �

Xn

i¼1

Xm

j¼1

Mjgi

" #�1

ðA:8Þ

To obtain Eq. (A.9);

Xm

j¼1

Mjgi ðA:9Þ

Perform the ‘‘fuzzy addition operation’’ of m extent analysis valuesfor a particular matrix given in Eq. (A.10) below, at the end step ofcalculation, new (l, m, u) set is obtained and used for the next:

Xm

j¼1

Mjgi ¼

Xm

j¼1

lj

Xm

j¼1

mj

Xm

j¼1

uj

!ðA:10Þ

where l is the lower limit value, m is the most promising value and uis the upper limit value. And to obtain Eq. (A.11);


Xn

i¼1

Xm

j¼1

Mjgi

" #�1

ðA:11Þ

Perform the ‘‘fuzzy addition operation’’ of Mjgi (j = 1, 2, 3, 4, 5, . . ., m)

values give as Eq. (A.12):Xn

i¼1

Xm

j¼1

Mjgi ¼

Xn

i¼1

lj

Xn

i¼1

mj

Xn

i¼1

uj

!ðA:12Þ

And then compute the inverse of the vector in Eqs. (A.12) and (A.13)is then obtained such that

Xn

i¼1

Xm

j¼1

Mjgi

" #�1

¼ 1=Xn

i¼1

ui;1=Xn

i¼1

mi;1=Xn

i¼1

li

" #ðA:13Þ

Step 2: The degree of possibility ofM2 = (l2, m2, u2) P M1 = (l1, m1, u1) is defined as Eq. (A.14):

VðM2 � M1Þ ¼ supY�x½minðlM1ðxÞ;lM2ðyÞÞ� ðA:14Þ

And x and y are the values on the axis of membership function ofeach criterion. This expression can be equivalently written as givenin Eq. (A.15) below:

VðM2 P M1Þ ¼1 if m2 P m1;

0 if l2 P u1;l1�u2

ðm2�u2Þðm1�u1ÞOtherwise

8><>: ðA:15Þ

To compare M1 and M2; we need both the values of V(M2 P M1)and V(M1 P M2):

Step 3: The degree possibility for a convex fuzzy number to begreater than k convex fuzzy numbers

Mi ði ¼ 1;2;3; . . . ; kÞ can be defined by VðM � M1;M2; . . . ;MkÞ¼ V ½ðM � M1Þ&ðM � M2Þ& � � �&ðM � MkÞ� ¼min VðM � MiÞ;

i ¼ 1;2; . . . ; k

Assume that Eq. (A.16) is

dlðAiÞ ¼min VðSi � SkÞ ðA:16Þ

For k = 1, 2, 3, 4, 5, . . ., n; k – i. Then the weight vector is given byEq. (A.17):

Wl ¼ ðdlðA1Þ;dlðA2Þ; . . . ;dlðAnÞÞT ðA:17Þ

where Ai (i = 1, 2, 3, 4, 5, 6, . . ., n) are n elements.Step 4: Via normalization, the normalized weight vectors are gi-

ven in Eq. (A.18):

Wl ¼ ðdðA1Þ;dðA2Þ; . . . ; dðAnÞÞT ðA:18Þ

where W is non-fuzzy numbers.

A.3. Entropy method for competitive priority ratings

In our HOE model, step 3 is to obtain and analyze the followingexcellence comparison matrix:

where xml is the performance of company C1’s on organizationalneeds for excellence (ONE) Wm, perceived by the experts. Basedon this X information, the company C1 may set priorities on the Morganizational needs for excellence in order to achieve a relativecompetitive advantage over other companies. If company C1 per-

forms much better than any other companies in terms of organiza-tional needs for excellence Wm, then further improvement may notbe urgently needed and thus a lower priority could be assigned toWm. At the other extreme, if C1 performs much worse than manyother companies on Wm, then it may be difficult for C1 to build acompetitive advantage within a short period of time. In both cases,Wm could be assigned a lower priority rating. However, if most com-panies perform quite similarly on Wm, not too much improvementeffort from C1 may result in a better performance of its excellenceand give C1 a unique competitive advantage. Thus a higher prioritycould be assigned to Wm. In particular, if all companies’ perfor-mances on Wm are the same, it implies a great excellence opportu-nity since any improvement would create a significant competitiveadvantage. So the highest priority could be assigned to Wm. This ba-sis of assigning priorities is interestingly related to the entropy con-cept in information theory. Entropy is a measure for the amount ofinformation (or uncertainty, variations) represented by a discreteprobability distribution, p1, p2, . . ., pL:

EðW1Þ ¼ �UL

X1

l¼1

Pml lnðPmlÞ ðA:19Þ

where UL = 1/ln(L) is a normalization constant to guarantee0 6 E(p1, p2, . . ., pL) 6 1. Larger entropy or E(p1, p2, . . ., pL) value im-plies smaller variations among the pl’s and hence less informationcontained in the distribution. For the mth row of the customer com-parison matrix X corresponding to the organizational needs forexcellence need Wm; xm1; xm2; . . . ; xml, let xm ¼

Pxml be the total

score with respect to Wm. Then according to (A.19), the normalizedratings pml = xml/xm for l = 1, 2, . . ., L can be viewed as the ‘‘probabil-ity distribution’’ of Wm on the L companies with entropy as

EðW1Þ ¼ �UL

X1

l¼1

Pml lnðPmlÞ ¼ EðW1Þ

¼ �UL

X1

l¼1

xml lnðxml=xmÞ ðA:20Þ

It is clear that the larger the E(Wm) value, the less information con-tained in Wm or smaller variations among the pml’s (or xml’s). If allcompanies’ performance ratings on Wm, xm1, xm2, . . ., xmL, are thesame, Wm has zero variations and E(Wm) achieves its maximum of1. So E(Wm) can be used to reflect the relative competitive advan-tage in terms of the organizational needs for excellence Wm. Allthese E(Wm) values, after normalization:

em ¼ EðWmÞXM

m¼1

EðWmÞ;,

m ¼ 1;2; . . . ;M ðA:21Þ

em can be considered as the excellence competitive priority ratingsfor company C1 on the M organizational needs for excellence, with alarger em indicating higher competitive priority for the correspond-ing Wm. For more on entropy and its applications (Chan, Kao, Ng, &Wu, 1999).

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EFQM Model by QFD Approach-2011

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