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Hindawi Publishing CorporationJournal of Industrial EngineeringVolume 2013, Article ID 682532, 11 pageshttp://dx.doi.org/10.1155/2013/682532

Review ArticleFuzzy Quality Function Deployment: An AnalyticalLiterature Review

Mohammad Abdolshah1 and Mohsen Moradi2

1 Department of Engineering Faculty, Islamic Azad University, Semnan Branch, P.O. Box 35136-93688, Semnan, Iran2Department of Industrial Engineering, Semnan University, Semnan, Iran

Correspondence should be addressed to Mohammad Abdolshah; [email protected]

Received 10 December 2012; Revised 1 April 2013; Accepted 8 April 2013

Academic Editor: Hsin-Hung Wu

Copyright © 2013 M. Abdolshah and M. Moradi. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

This paper presents an analytical literature review on fuzzy quality function deployment (FQFD) of papers published between2000 and 2011. In this review, publications were divided into two main groups. First group included publications which proposedsomemodels to develop FQFD.The second one was related to new applications of FQFDmodels. Next, publications were analyzedand research gaps and future directions were presented. We reached some conclusions including the following. (i) Most of studieswere focused on quantitative methods to accomplish phase 1 of QFD or House of Quality (HoQ). The most employed techniqueswere multicriteria decision making (MCDM)methods. (ii) Although main purpose of using QFDwas product development, otherfactors such as risk and competiveness analysis should be considered in product development process. (iii) A promising approachis using of metaheuristic methods for solving complicated problems of FQFD. (iv)There are a few studies on completing all phasesof FQFD.

1. Introduction

Quality function deployment (QFD) is a customer-drivenproduct development tool to achieve higher customer sat-isfaction through translating customer needs (CNs) intodesign requirements (DRs), part characteristics (PCs), andproduction plans and control [1]. Chan and Wu [2] definedQFD as “a system to assure that customer needs drive productdesign and production process.” QFD is used essentially inorder to design product according to customer favorites. Ageneral QFD process consists of 4 phases. First phase, whichis called House of Quality (HoQ), is an important stage indeploying QFD process. In this stage, after determining CNsand technical characteristics (TCs), relationships betweenCNs (Whats) and TCs (Hows) as well as their interdependen-cies are established and their importance weight is calculated[1]. In second phase TCs are translated to important PCs.Critical parameters of process are established in third stageand finally production requirements are specified (fourthphase) [3].

Most of required data in QFD processes and activities areexpressed in natural language. Customers, for example, say

their expectations from product by using expressions such as“easy to use,” “safe,” and “comfortable” which all of them haveambiguity. Computing these ambiguities in a requirement isan important issue [4]. Using tools from fuzzy sets and theirconcepts, we can approximate linguistic data to a numericprecision [5]. This review, consisting of a bank with morethan 70 papers, divided publications in two main groups.First was publications combining FQFD with other methodsto develop its efficiency and effectiveness, and latter waspublications in new and major applications of FQFD. Eachof these two groups was divided into subgroups. In the 2ndsection we discuss these two groups, respectively. Discussedpapers were analyzed in the fourth section and the literaturevacancies were described.

2. Proposed Models for Developing FQFD

There are lots of models proposed to develop FQFD. Infact, according to wide aspects of deploying fuzzy QFD, itmay be used in combination with many other methods andmodels. All of these models are common in using fuzzy logic.

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Methods fordeveloping FQFD

Linear and nonlinearprogramming models

Metaheuristicmethods

Fuzzy regression models(linear and nonlinear)

Hybrid models

MCDM

Fuzzy groupdecision making

models

Multi criteriadecision making(MCDM) modelsModels proposed

to prioritizeCRs

Other methods

Linear andnonlinear

programming

Figure 1: Categorizing the models for developing FQFD.

Here, according to the literature, this group is divided into5 following subgroups to be discussed. Then as shown inFigure 1 these 5 groups can be discussed in 8 different groups.

2.1. Fuzzy Linear and Nonlinear ProgrammingModels. Linearprogramming methods have been widely used in FQFD.These models are mostly employed to compute fulfillmentlevels of process parameters (e.g., TCs, PCs) respecting someconstraints like budget, technical difficulty, and technology.Followings are some presented models in the literature.

Ko and Chen [6] presented a fuzzy linear programming(FLP) using QFD and fuzzy normal relations evaluationmethod for new product development (NPD).They used FLPto determine fulfillment levels of engineering characteristicsand design requirements to maximize customer satisfactionwith respect to company’s sources, technical difficulties, andmarket competition constraints. In this model, in orderto identify relationships between CRs and DRs as well asinterdependencies within DRs, Wassermann’s equation hasbeen used. Also relationships between characteristics wererepresented by fuzzy number to cope with fuzziness. In thatmodel, percentage increase of costs corresponding to increaseof fulfillment levels of each TC was specified and total costscould not be more than a given value. Minimum fulfillmentlevel of each DR was known and fulfillment level of DRcouldn’t be less.

Luo et al. [7] proposed an optimization method basedon FQFD for part selecting. Their model became an integerlinear programmingmodel at the end.Thismodel consideredCNs as fuzzy numbers and translated them to TCs andfinally to PCs.Their final goal wasminimizing the differencesbetween CNs and end product. Chen and Ko [1] proposedfuzzy linear programming models to determine the fulfill-ment levels of PCs and failure modes and effect analyses

(FMEA) for risk analysis. This paper proposed FLP modelsfor determining fulfillment levels of PCs to achieve givenlevels of DRs to satisfy customers and considered both phases1 and 2 of NPD. Furthermore, to reduce risk FMEA as a con-straint for models in QFD process was introduced. To assurecustomer satisfaction, this paper considered fulfillment levelof 𝐽th DR in phase 2 more than or equal to that in phase 1.

Chen and Ko [3], in other study, considered close rela-tionships of 4 phases of QFD by means of end chain concept(MEC) and proposed a set of FLP models to determinecontribution level of HOWs in customer satisfaction. Theyalso used FMEA inphases 2 and 3 and risk evaluation in phase4, to solve potential risk problem in NPD project. In additionto DRs, in these 4 phases approach PCs, process planning(PP), and process requirements (PRs) have been considered.Solving risk problem in NPD, the authors used fuzzy FMEAas a model constraint for risk evaluation of DRs, PCs, PPs,and PRs in 4 phases. Reliability of models’ results dependedon reliability of relations between variables and parametersin HoQ, as they said. It was proposed to apply other conceptssuch as Kano’s model instead of MEC in QFD modeling.

In addition to FLP models, one can use fuzzy nonlinearprogramming (FNLP) models in QFD. Although using thesemodels makes it more difficult, they have a better efficiencyin sophisticated problem, because in such problems, relationsare often non-linear.

Chen and Ko [6] presented an FNLP model in combina-tion with Kano’s model to specify performance levels of DR.They classified DRs in three groups of exciting, functional,and basic using Kano’s model. This FNLP was based onFLP model of Chen and Weng (2003). They illustrated withan example that their model was better than correspondingNLP model. Sener and Karsak [8] proposed a hybrid modeldiscussed in Sections 2.2–2.8.

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2.2. Multicriteria Decision Making (MCDM) Models. It canbe claimed that among quantitative models used in FQFD,MCDMmodels are themost used ones.Thesemodels inQFDmatrixes (often HoQ) are applied in determining importanceof parameters.

Kim et al. [9] used MCDM models for calculating targetvalues of TCs as an optimization problem. Target functionwas finding target value levels of TCs in such a way thatcustomer satisfaction became maximized for target functionvalues.

Kwong and Bay [10] employed fuzzy analytical hierarchyprocess (FAHP) to weight customer needs. They proposed afuzzymodel for weightingCNs based on fuzzymeasurementsand traditional AHP. They used triangular fuzzy numbers(TFNs). Two advantages of their model was (1) using fuzzynumbers to deal with ambiguous nature of human judgmentsand (2) adoption of fuzzy numbers allowed designing teamto have freedom of estimation regarding overall goal ofcustomer satisfaction. These estimations could be optimisticor pessimistic.

Erol and Ferrell [11] proposed a method to select amongfinite alternatives when there are more than one object anddata are both quantitative and qualitative. FQFD was usedto convert qualitative data to quantitative ones; then thisdata was combined with other quantitative one to constructmultiobjective mathematical model. A modified version ofpreemptive goal programming was used to solve the modeland some solutions close to ideal solution were provided fordecision makers to make final decision.

Buyukozkan et al. [12] employed fuzzy analytical networkprocess (FANP) to determineDRweights.They used TFNs intheirmodel. Although FANPproposed in this paper hasmorecalculations than other methods such as FAHP and is moretime consuming, results are more precise and, consideringlong-term competiveness, it is more useful for companies.

Kahraman et al. [5] purposed a fuzzy optimizationmodelfor specifying product’s technical requirement which shouldbe considered during design process in QFD. Consideringinterdependencies, target function’s coefficient were calcu-lated using FANP. Doing the samewith FAHP, they comparedresults of both methods. Although results were close becauseof relations within TCs, apparently, FANP had better credi-bility.

Chen andWeng [13] presented an approach for evaluatingof TCs in FQFD using goal programming models. Theyemployed fuzzy goal programming to identify fulfillmentlevels of DRs according to constraints such as cost, customersatisfaction, and technical difficulties. Coefficients of goalprogramming were fuzzy to reflect ambiguity in linguisticdata. This model also considered business competiveness indetermining minimum fulfillment level of DRs. This modelused 𝛼-Cuts to determine membership function of fuzzygoals and fulfillment levels of DRs.

Gunasekaran et al. [14] purposed an MCDM methodusing Monte Carlo simulation and FQFD for optimizationof supply chain management (SCM). Here, customer needswere simulated by FQFD, Monte Carlo simulation, and amultiobjective model for optimizing customer preferences.

By presenting some novel criteria such as design com-plexity, speed of construction, environment, and aestheticand construction complexity, Mousavi et al. [15] developeda hybrid FQFD model using fuzzy technique for orderperformance by similarity to Ideal Solution (TOPSIS) forbridge scheme selecting, which was a complex engineeringproject.

Lin and Lee [16] presented a model of QFD and FANP tosolve NPD problem in TFT-LCD industry. They used fuzzyDelphi method to simplify number of factors in the modeland made paired comparisons easier.

Celik et al. [17] proposed an integrated FQFD modelfor investment decisions of shipping routing in crude oiltankermarket.Thismodel used FQFDprinciples for shippinginvestment process through substituting HoQ with ship ofquality (SoQ). In addition, FAHP and fuzzy axiomatic design(FAD) algorithms were integrated in SoQ in order to reachquantitative results. SoQ framework was tested by a set ofperiodic data and current trends in tankers market for bigcompanies like Suezmaxes and Aftermaxes.

Khademi-Zare et al. [18] provided two prioritizing mod-els based on FQFD for ranking strategic actions (SAs) ofmobile cellular telecommunication in Iran. Considering thegap between current situation and positive ideal situationfor customer attribute (CAs), they used TOPSIS for rankingCAs in first model. Using AHP in the second model, morefactors have been considered to rank CAs. A fuzzy utilityfactor, 𝐵𝑗, was introduced for ranking CAs in both models.Bothmodels were able to consider ambiguity in human judg-ments and allowed customer-oriented companies likeMobileCommunication of Iran (MCI) to use voice of customers forextracting benefits in a more expressive way.

Lin et al. [19] presented amodel to analyze environmentalrequirements of products with linguistic preferences usingFQFD and ANP with interdependence relations betweenenvironmental production requirements (EPRs) and sustain-able product indicators (SPIs). At first, to facilitate mainissue of QFD problem, Whats questions of EPRs and Howsquestions of SPIs have been made, which were two maincomponents of HoQ. In conjunction with fuzzy set theoryand ANP, the systemic analytical approaches were proposed.

Mousavi [15] proposed a systematic decisionmaking pro-cess for evaluating conceptual bridge design and selecting thebest idea through the new methodology based on integratedoptimization. In the first phase, QFD has been employed totranslate project requirements to design requirements. Then,the best structure as an alternative was selected by TOPSISbased on weighted criteria achieved from first phase. In thisstudy the rating values regarding each alternative and criteriathroughout the phases were described in a fuzzy environmentby means of linguistic variables. Finally, a case study wasprovided to illustrate the implementation process of theintegrated methodology for bridge superstructure design.

In his work,Wang [20] utilized FQFD andMCDMmeth-ods for optimized modular design. This article integratedFAHP and fuzzy decision making trial and evaluation labo-ratory (DEMATEL) to achieve weights of DRs and createdconstructing blocks of optimummodular design.


Nepal et al. [21] proposed an FAHP framework accordingto fuzzy set theory, to prioritize customer satisfaction (CS)attributes in target planning for vehicle design. In addition,unlike previous studies, they considered a broad range oftechnical and strategic factors to evaluateweights.Then, theseweights were introduced in target planning by identifyingthe gap in current CS level. This framework was deployed inMicrosoft Excel, so it could be utilized with a limited trainingin organization. Unlike traditional AHP, FAHP gave QFDteammembers freedom of estimation, since judgments couldvary from very optimistic to very pessimistic.

Zheng et al. [22] presented an evaluation method forintegrating fourth-party logistics (4PL) supply chain based onFQFD and AHP. At first, they studied 4PL requirements andintroduced them into supply chain design process by QFD.Then, nonvalue processes were eliminated using benchmark-ing method and supply chain was rebuilt by supply chainfuzzy evaluation theory. Utilizing multiobjective optimizingtheory, optimum model of 4PL was achieved by evaluatingvalue. Finally, they used FAHP to present evaluation methodof 4PL integration.

Gungor et al. [23] proposed a fuzzy decision makingsystem (FDMS) based on fuzzy control rules for new productdesign using FQFD. Customer needs, as inputs of FDMS,were fuzzified and prioritized using membership functionconcept. DRs were rated by if-then rules. This paper deter-mined fulfillment levels of DRs by FANP and its results werecompared with those of proposed FDMS.

Lee and Lin [24] proposed a fuzzy model consisting ofFANP and QFD in new product development. Parametersof each phase of QFD were determined through literaturereview and interviewing with experts and fuzzy Delphimethod was utilized for screening important factors. Alsofor constructing relations within HoQ, fuzzy interpretivestructural modeling with FANP was used.

Liu et al. [25] prepared a model for product design andselection using FQFD andMCDMmethods.This paper inte-grated FQFD and product prototype selection and proposeda method for product design selection. 𝛼-Cuts operation wasutilized in QFD for fuzzy set calculations of each component.Competitive analyses as well as relations between TCs wereconsidered too. Considering TCs and product developmentfactors in product prototype selection, an MCDM methodwas proposed for prototype selection. This method providedproduct developers with useful information and preciseanalysis. It is a useful decision aid tool.

Liu and Wang [26] proposed an advanced QFD modelwith FANP to consider interdependencies within and rela-tions between QFD components. To extend applicability ofQFD, authors were seeking to extend QFD from productplanning phase for part deploying phase.

Zandi and Tavana [27] proposed a methodology for eval-uation and selection of the best e-CRM framework in agileproduction using FQFD, fuzzy group real option analysis(ROA), and fuzzy TOPSISmethod. First, e-CRM frameworkswere prioritized according to financially oriented characteris-tics using an ROAmodel. Next, the e-CRM frameworks wereranked according to their customer-oriented characteristicsusing a hybrid fuzzy group permutation and a four-phase

fuzzy quality function deployment (QFD)model with respectto three main perspectives of agile manufacturing (i.e.,strategic, operational, and functional agilities). Finally, thebest agile e-CRM framework was selected using a TOPSISmodel.

Zarei et al. [28] proposed a hybrid methodology of AHP-QFD to increase leanness of food supply chain. Linking leanattributes (LAs) and lean enablers (LEs), this study usedquality function deployment (QFD) to identify viable LEs tobe practically implemented in order to increase the leannessof the food chain. Furthermore, triangular fuzzy numberswere used to deal with linguistic judgments expressingrelationships and correlations required in QFD. FAHP wasemployed to prioritize LAs.

Yousefie et al. [29] proposed an original approach forthe management tools selection based on the quality func-tion deployment (QFD) approach. Specifically, the researchaddressed the issue of how to deploy the house of quality(HOQ) to effectively and efficiently improve managementtools selection processes and thus company satisfactionabout its excellence achievement. Entropy method was usedto perform competitive analysis and calculation weightsof competitive priorities. Then, FAHP was employed toprioritize management tools. Presented hybrid models andmethodologies by Karsak and Ozogul [30], Huang and Li[31], Wang and Chin [32], Tolga and Alptekin [33], Karsaket al. [34], and Ozdemir and Ayag [35] were discussed inSection 2.8.

2.3. Fuzzy Group Decision Making Models. Zhang and Chu[36] introduced a model for selection of multiple designschemes of complex products based onQFD and fuzzy groupdecision making. They divided the scheme to parts andevaluated weights of each part using group decision makingin QFD and rank the designs. In addition to selection ofoptimized design, QFD was employed to integrate customerrequirements into design.This paper also used groupdecisionmaking for determining relations between characteristics.

Buyukozkana et al. [37] proposed a new fuzzy groupdecision making method for product design by QFD. In theirmodel, inputs of QFD process were both quantitative andqualitative. Quantitative data was translated to qualitativeone; then fuzzified and finally weights of TCs were computed.

Liu [38] used a fuzzy group decision making withrisk-taking attribute to deploy QFD, because they thoughtdecision makers’ opinions about risk taking were different(optimistic, normal, and conservative). Finally, TCs wereprioritized and HoQ was completed.

Zhang and Chu [36] proposed a fuzzy group decisionmaking to aggregate multiformat and multigranularity judg-ments of decision makers based on two optimizing models(logarithmic least square and weighted least square) forconstructing HoQ. Logarithmic least square model and twonormalized formulae were utilized to solve fuzzy paired com-parisons matrices and normalizing evaluations in linguisticsformat. Then, weighted least square model was used toaggregate ultimate normalized multigranularity evaluations.Sanayei et al. [39] utilized MCDM model of Vlse Kriter-ijumska Optimizacija I Kompromisno Resenje (VIKOR),


which means multicriteria optimization and compromisesolution, in association with fuzzy group decisionmaking forsupplier selection. VIKOR method is a multicriteria decisionmakingmethod for solving problemswhich have inconsistentcriteria. It selects an alternative which has the least distancefrom ideal solution.

Lin et al. [40] employed QFD and fuzzy group deci-sion making for service innovation. This study developeda scientific framework for tourism service managementfrom epistemology perspective. This article used FQFD andfuzzy group decision making to analyze various serviceevaluation criteria. In particular, the methodology allowedthe identification of service attributes perceived to affectservice design performances from the tourist’s point of view,enabling the assessment of possible gaps between tourists’and hotel’s perception of service delivery. To assess viablestrategic designs, in the proposed approach they introduceda utility factor, considering the costs of implementation foreach “How.” Wanga and Xionga [41] provided an integratedapproach of group decision making based on linguisticvariables for QFD applications. Proposed methodology doesits computations with words—without converting to fuzzynumbers—so information loss risk is low. Hybrid model ofLiu et al. [26] is discussed in Section 2.8.

2.4. Metaheuristic Methods. According to meta-heuristicalgorithms’ ability to solve complex problems, they can beused in different stages of QFD process. Discussed arti-cles often use meta-heuristic methods to identify relationsbetween CNs and TCs.

Hsiao and Liu [42] proposed a neurofuzzy evolutionaryapproach for product design. Their model was based onartificial intelligence including fuzzy theory, backpropagationneural network (BPN), and genetic algorithm (GA), alongwithmorphological analysis to synthesize, evaluate, and opti-mizate of product design. Fuzzy logic was utilized for mod-eling imprecise market information, BPN for determiningrelations betweenCNs anddesign parameters,morphologicalanalysis for constructing design alternatives, and finally GAwas used to select optimum design.

Lin et al. [40] prepared an intelligence model to estimateproduct design time (PDT) using intelligence method. Dueto lack of information in early stage of product development,this model utilized fuzzy logic to complete HoQ. This modelemployed QFD to extract DRs from CNs. Then a fuzzyneural network was built to combine data and estimatedPDT, which made use of fuzzy comprehensive evaluation tosimplify structure. In a word, the whole estimation methodconsisted of four steps: time factors identification, productcharacteristics extraction by QFD and function mappingpattern, FNN learning, and PDT estimation. Finally, toillustrate the procedure of the estimation method, the caseof injection mold design was studied. This model had somelimitations. It was inapplicable for developing brand newproducts, because the influencing weights of linguistic vari-ables obtained by experience or experiment were importantfor the parsimonious FNN model.

As the functional relationships between customerrequirements and engineering characteristics in QFD are

uncertain, unclear, and fuzzy, Huang and Li [31] proposedradial basis function (RBF) to determine the functionalrelationships for QFD and QFD functional relationshipsmodel based on RBF. According to RBF neural network,nonlinear mapping space from the input space to the outputcan be realized, and optimal relationships pattern of the inputand output would be obtained. The customer requirementsand engineering characteristics in QFD constituted the inputand output of the RBF.

The optimal relationships were constructed through theneural network training. Wanga and Xionga [41] analyzedthe limitations of traditional methods by using the prod-uct planning HoQ; the available linguistic terms based onexperts’ knowledge, and with the artificial neural network,were introduced to realize neural network-based fuzzy rea-soning. The final importance of the technical requirementswas evaluated reasonably and effectively. This model consid-ered market competitiveness and technical competitiveness.The importance of technical requirement was determinedthrough four steps: (1) acquisition of customer requirementsand ranking of their importance measures, (2) establishmentof technical requirements and their relations with customerrequirements, (3) establishment of market competitivenessand technical competitiveness, and (4) evaluation of technicalrequirement importance with competitiveness.

Lee and Lin [24] proposed an ANN-based dynamicFQFDmethod in order to solve the dynamic and fuzzy natureof QFD; they solved the problem of the dynamic natureby using neural network method, while trapezoidal fuzzynumber for its ambiguity was introduced. Firstly, a combinedmethod with neural networks and FQFD was established;after learning and training, the method could quickly andeffectively deliver customer requirements to the productsdesigners. This model could solve a non-linear problem andmet the non-linear changes in customer needs. Hybridmodelof Liu [25] and Karsak and Ozogul [30] paper were describedin Section 2.8.

2.5. Fuzzy Regression Models (Linear and Nonlinear). Thesemodels were used to complete HoQ and their main focus wason finding relations between parameters of HoQ. Chen andChen [13] utilized a fuzzy nonlinear possibilistic regressionapproach to model product planning (first phase of QFD).The model was able to incorporate both qualitative andquantitative data in determining relations between CRs andDRs as well as within DRs. Using linear regression, somecoefficients became crisp because of linear programmingnature. Therefore, they used nonlinear regression in theirmodel. TFNs were used in this model. Sener and Karsak [8]and Karsak et al. [34] studies were discussed in Section 2.8.

2.6. Models Proposed to Prioritize CRs. Identifying CNsand their importance weight is first step of QFD process.Due to its importance, some papers have been focused ondetermining CNs and their importance weight. Lai et al.[43] proposed a new methodology in their articles whichconsidered competitors information. This paper consideredcompetitive environment and product’s current performance.


The proposed algorithm was complicated, but the authorsclaimed that its solutions were more accurate than similarmodels. Mehdizadeh [44] used fuzzy centroid-based methodand considered competitive environment to rank CRs. Heemployed both normal and nonnormal numbers. All ofprevious studies employed normal fuzzy numbers whichresulted in misleading solutions in case of non-normal fuzzynumbers. Using fuzzy centroid-based method, this papersolved the problem.

2.7. Hybrid Models. Some of discussed models encompassmore than one model of above ones, so we discussedthem under subgroup of hybrid models. Using fuzzy settheory and Euclidean distance, Guo et al. [45] put forwarda methodology called Euclidean space distances weightingranking method. They applied this method in fuzzy AHP.This method satisfied additive consistent fuzzy matrix. Inaddition to designing an algorithm for calculating weights,they developed a module to design new product based oncustomer needs weight calculation model. Sener and Karsak[8] used an optimization model and fuzzy regression onthe basis of non-linear programming to determine targetvalues of TR. Using linear models, regression coefficientsapproached to zero and, due to this, they used nonlinearmodels to determine functional relationships between CRsandTRs andwithin them. Finally, using a fuzzymathematicalprogrammingmodel, target levels of TRs have been achieved.This fuzzy mathematical programming model incorporatedboth center values and spread values of parametric estima-tions of functional relationships in optimization process and,thus, avoided loss of information in design phase.

Liu [25] used a fuzzy group decision making modelin association with genetic algorithm in QFD process tocompleteHoQ and calculation of weights of TRs.The rankingwas done in two phases according to budget and timeconstraints.

Huang and Li [31] employed BP neural network andAHP in QFD fuzzy to determine key technology of productplanning and designing. First, they used AHP to determineweights of CRs and then, as the relationships between TRsandCRs, TRs and part characteristics inQFDwere uncertain,nonlinear and fuzzy BP neural network was presented todetermine these relationships. After determining weights,part characteristics were identified using QFD and keytechnology of production was selected.

Karsak and Ozogul [30], in a paper titled an integrateddecision making approach for ERP system selection, theypresented a newdecision framework for selecting ERP systembased onQFD, fuzzy linear regression, and zero-one goal pro-gramming (ZOGP). Proposed framework considered bothcompany requirements and features of ERP system andprovided a tool not only to link company requirementsand features of ERP system but also to interact betweenthem through applying QFD principles. Using fuzzy linearregression, target level of ERP characteristics and reachablemaximum values of CNs were achieved. Finally, ZOGP wasemployed to select ERP system which minimized weightsaggregate of deviations from reachable maximum values oforganizational needs.

Wang andChin [32], used fuzzyAHP to prioritize charac-teristics inQFD.Thismodel utilized linear goal programming(LGP) to calculate normalized fuzzy weights and pairwisecomparisons matrices of AHP. Proposed LGP method wastested by three numerical examples including new productdevelopment (NPD). The results showed that LGP methodcan derive precise fuzzy weights for perfectly consistent fuzzycomparison matrices and normalized optimal fuzzy weightsfor inconsistent fuzzy comparison matrices on the basis ofminimum deviation. It was also shown that fuzzy AHP canbe used as a very useful decision support tool for NPDprojectscreening.

Mu et al. [46] presented an integratedmodel of fuzzymul-tiobjective model and Kano’s model to determine nonlinearrelationships in HoQ matrix. They considered firm’s budgetas a constraint for themodel and utilized fuzzymultiobjectivemodel to achieve maximum customer satisfaction consider-ing the constraint.

Karsak et al. [34] utilized ANP and GP in QFD to selectTRs, emphasizing design process according to given targets.This algorithm included two phases. At the first phase, HoQwas constructed usingANP, and at the second phase, TRs thatshould be focused were determined using ZOGP.

Tolga and Alptekin [33] integrated QFD with fuzzy com-promise-based goal programming to identify how muchproduct features should be improved. ANP is utilized toevaluate inner dependencies within customer needs, productattributes, and also the relationships between them.The con-straints of compromise-based goal programming includedmanufacturer budget and product competitive performancein market.

Ozdemir andAyag [35] proposed amodel forNPDmulti-objective problem and its solution with intelligent approach.They combined multiattribute method of TOPSIS and mul-tiobjective method of goal programming (GP) as well aseconomic analysis. Economic analysis was used for demandchanges during product life cycle. Fuzzy equivalent worth ofeach new product alternative was determined using fuzzy lifecycle monetary input, and results were input to the multi-criteria analysis. The selected multicriteria analysis tool wasTOPSIS in the paper and some additional judgmental criteriawere also considered to rank new product alternatives. Thepreference weights used to rank alternatives were fed to agoal programming model which made an ultimate selectionof new product(s) to be produced in the manufacturingsystem under capacity, sales potential, and workforce balanceconstraints. The goal programming model had an objectiveof minimizing the weighted sum of positive deviation fromtarget of total cost and negative deviation of target totalpreferenceweights.Theproposed approachwas implementedusing real data of a continuous production system for theneed of introducing new products into market.

Wang [20] integrated QFD with multiattribute decisionmaking (MCDM) models for optimal modular design. Thispaper combined fuzzy AHP and fuzzy DEMATEL to developmarketing-driven product. Fuzzy AHP was used to deter-mine important weight of CNs; then fuzzy DEMATEL wasapplied to achieve design requirements and to construct


common/specific building blocks for achieving an optimalmodular design.

Gungor et al. [23] developed a fuzzy decision makingsystem (FDMS) based on fuzzy control rules to designnew product using QFD which considered CNs as factors.Customer needs were determined as input variables andfuzzified using membership function concept. Weights ofthese factors were fuzzified to ensure the consistency of thedecisionmaker while assigning the importance of each factorover another. By applying IF-THENdecision rules, DRs of thefirmwere scored.This paper also used fuzzy analytic-networkprocess (FANP) to determine the fulfillment levels of DRs ofthe firm and its results were compared with FDMS’s ones.

Sanayei et al. [39] used MCDM model of VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje meansmulticriteria optimization and compromise solution) alongwith group decision making methods for selecting supplier.VIKOR was an MCDM method for solving problems withconflicting and noncommensurable (different units) criteriaand choosing the alternative that was closest to ideal solution.

2.8. Other Methods. Kahraman et al. [5] used fuzzy rankingmethods for evaluating DRs. Fuzzy ranking methods wererarely used in previous studies. This paper used three fuzzyranking methods in PVC windows industry and finallycompared them using sensitivity analysis.

3. Applications of FQFD

Some of papers discussed FQFD applications. AlthoughQFDhad numerous applications and traditional QFD has beendeployed in lots of firms, here we discussed most importantapplications of FQFD. The most important application ofQFD was supply chain management (SCM). Moreover, therewere some papers in product design and other applications.

3.1. Supply Chain Management (SCM). In a paper aboutselecting supplier, Bevilacqua et al. [47] proposed a newmethodwhich translatedHoQ as problem of a huge companyof clutch coupling production.

Zhang and Chu [48] put forward a new method for con-tingency management of 3rd party logistics (3PL). Theysuggested a multiobject framework using FQFD and groupdecision making. In addition to contingency management,risk management was also considered.

Wang et al. [49] proposed a practicable method fordetermining customization place of service products logisticsto improve capability of 3PL customized servicing. First,they used FQFD to import CNs in locating process. Then,a multiobject integer programming model was developed todetermine optimum locating program under constraints of3PL interior sources and payment values of buyer firm foroutsourcing.

Rau and Fang [50] proposed a combined model of FQFDand TRIZ to solve conflicts within design characteristicsfor packaging design in notebooks logistic. In this model,first, design requirements and design characteristics werespecified. Then their relationships were identified based on

language variables using experts’ opinions usingHoQ.Weightof design characteristics was calculated using fuzzy integralmethod. Finally, modifications and innovation principlesare determined for solving conflicts of product pack designproblem corresponding to priority of design characteristics.

Bottani and Rizzi [51] suggested a method for customerservice management in supply chain using FQFD. The mainintention was using HoQ to improve logistics processeseffectively and efficiently. They estimated distance betweenfirm’s performance in logistic services and customer needs.According to customer importance, CNs were weighted toidentify key factors for services improvement. The studybeganwith identifying characteristics which purchased prod-uct for satisfying customers (what variables). Then it createdcriteria for supplier evaluation using a final ranking basedon fuzzy proportion index for conclusion. All of processwas deployed using fuzzy numbers. Using a fuzzy algorithmallowed company to define importance weights of What,relationship rates between Whats and Hows, and effect ofeach potential supplier by language variables. This paperattended specifically to different personal evaluations in HoQaccomplishment process and fuzzy triangular numbers weresuggested to consider ambiguity in language evaluations.

Amin and Razmi [52] suggested a fuzzy integratedmodel based on firm’s strategy for managing, evaluation,and supplier development. In the first phase, QFD was usedto rank the best ISPs based on qualitative criteria. In thefirst phase, QFD was employed for ranking best ISPs basedon qualitative criteria. Then, a quantitative model was usedto consider quantitative criteria. Finally, two models weredeveloped and the best ISP was selected. In the next phase,a new algorithm was developed to evaluate selected ISPsin three features: customer, performance, and competition.Fuzzy logic and triangular fuzzy numbers were used fordealing with ambiguity in human thinking. A case studybrought forward to show evaluation and selection phases ofISPs.

Sohn and Choi [53] used QFD for SCM consideringreliability.They developed an FQFDmodel to determine rela-tionships between customer needs and design characteristicsaccording to reliability test. They employ QFD to determinerelationships between CNs and design characteristics in eachsupply chain. Then, they assumed these relationships werereversible and considered end user needs as a function ofreliability test performance variables in the last chain ofproduct design process. Finally, fuzzy MCDMwas utilized tofind an optimum solution set regarding demanded reliabilityperformance. Other works including Gunasekaran et al. [14],Lin et al. [40], Zheng et al. [22], and Zarei et al. [28] werediscussed in Section 2.

3.2. Product Design. Due to law restrictions and publicpressures, many companies were making products consistentwith environmental concerns. Many of these products wereproduced and sent to market so far, but most of them wererejected by customers and couldn’t gain a market share. Itseemed that they faced this problem because they just con-sidered environmental conditions and neglected customer


needs [54]. Recently, some studies have been focused on thisproblem to solve it by considering both environmental andcustomer requirements simultaneously using FQFD.

In a paper titled as “Integration of environmental consid-erations in quality function deployment by using fuzzy logic,”Kuo et al. [54] proposed a developed Eco-QFD model toformadesign teamconsidering both environmental concernsand customer satisfaction by using QFD. A fuzzy groupmethod was applied to Eco-QFD for product developmentplanning to reduce the vagueness and uncertainty in a groupdecision making process. This fuzzy multiobjective modelnot only considered the overall customer satisfaction butalso encouraged enterprises to produce an environmentallyfriendly product. With an interactive approach, the optimalbalance between environmental acceptability and overallcustomer satisfaction could be obtained. Finally, a casestudy illustrating the application of the proposed model wasalso provided. Kuo and Hung [55] proposed a fuzzy Eco-QFD model to design products based on environmentalconsiderations. They used a fuzzy multiobjective model toaid the design team in choosing target levels for engineeringcharacteristics. Paper of Lin et al. [40] about environmentalproduction was discussed in Section 2.2.

Lin et al. [40] employed FQFD to analyze Island hotelsmanagement by using lingual preferences. They proposed anapproach for managing internal and external services as wellas service innovations based on an FQFD framework. Thatwas a methodology adapted to service development properly.The paper addressed how to apply HoQ to improve hotel ser-vices innovation process and tourists satisfaction efficientlyand effectively. Fuzzy logic provided a methodology to dealwith lingual judgments nature inHoQ.At the end a case studywas discussed to examine model accuracy. Work of Lin et al.[40] on service innovation was discussed in Section 2.3.

3.3. Other Applications. Jia and Bai [56] suggested a methodto develop manufacturing strategy using QFD. This methodconsisted of 11 steps and used QFD as a transferring toolto relate competitive factors with manufacturing decisiongroups (such as structural and infrastructural decisiongroups) and a main tool in different stages of developingmanufacturing strategy. This paper also integrated fuzzy settheory with HoQ to deal with vagueness of decision processinputs.

Sen and Baraclı [57] presented a methodology to findsoftware selection requirements for organizations based onFQFD. They proposed a QFD approach to determine whichnonfunctional requirements reported by recent studies wereimportant in software selection decision making and com-bined it with functional requirements.The proposed solutionnot only helped decision makers to determine softwareselection’s characteristics and criteria, but also provided theirimportance weights.

Zhang and Chu [48], using rough set theory, proposeda new methodology for FQFD to facilitate decision makingin early stages of product development. At the end, theycompared their proposedmethod with traditional FQFD andfound result of proposed method more accurate.

Table 1: The frequency of proposed methodologies for FQFD.

ID Method Number ofresearch

1 Fuzzy linear and nonlinear programmingmodels 53

2 Fuzzy regression models (linear andnonlinear) 3

3 Metaheuristic methods 64 Hybrid models 135 Fuzzy group decision making models 8

6 Multicriteria decision making (MCDM)models 29

7 Models proposed to prioritize CRs 28 Other methods 1

Yan et al. [58] employed FQFD to design decision supportsystem for hazardous material road transportation accidents.First, they identified problem of decision support system planselection including requirements of governmental organiza-tion and transportation companies. Then, they used QFD totranslate these requirements to technical characteristics ofdecision support system.

4. Conclusion

As mentioned, nowadays FQFD is used as a powerful toolin designing and developing products and decision makingfrom supplier selection to Eco-design product development.In comparison with traditional QFD, using fuzzy logic isunavoidable. Actually, regarding application of lingual vari-ables in paired comparisons, ratings, and weightings, usingcrisp numbers leads to lose information. Combining fuzzylogic and QFD has made a new methodology named fuzzyQFD. Usingmany case studies showed that this methodologyis effective and reliable. In this review, after analyzing paperspublished between 2000 and 2011, we categorized papersin two major groups of (i) proposed models of FQFDand (ii) QFD employed applications. The first consisted of8 subgroups and the second topic included 3 subgroups.Literature review showed that FQFD has been used mostlyin supply chain management. After discussing and analyzingpapers, we identified models’ weakness, strength points,and literature vacancies and proposed some directions forfuture research. The frequency of proposed methodologiesfor FQFD (Table 1) showed that fuzzy linear and nonlinearprogramming models have been used 53 times with thefirst rank. Therefore it seems that since linear and nonlinearprogramming models can find the optimum solution, mostlyresearches tend to use this methodology.

5. Future Research

Most of discussed papers only studied first phase of QFD (i.e.,quality matrix) and just completed HoQ.This was maybe dueto importance of this stage, but to deliver a product according


to customer requirements in target market, we should com-plete all phases of QFD. Results of first phase were weighted(or prioritized) TCs which should be translated to partcharacteristics and process requirements, respectively, so wecould produce a product appropriate for targetmarket. Henceit seems essential to extend proposed models for all phasesof QFD. Regarding HoQ, other problem is its big size andcomplexity in many NPD problems. This issue, especially infuzzy environment, caused greater complexity. Metaheuristicalgorithms such as artificial neural networks (ANN), whichwere used to determine complex and non-liner relations,were one of feasible solutions. For this problem, most ofresearchers assumed that relations between TCs and CNs aswell as relations within TCs were linear in HoQ. On the otherhand, model’s applicability and being user-friendly weremore important than model. Introducing fuzzy logic withits comprehensive, long, and time-consuming calculation ofthe models has stopped many models in the theory phaseand does not allow these models to become practicable. Agood solution to this problem is to design some modules andsoftware which make calculations easier and allow users toget their outputs by a little change in inputs. Another wayto increase users’ interest in proposed models is using expertand decision support systemswhich decrease calculations. It’snoteworthy that designing databases is an effective way forgathering, maintenance, and easier access to CNs for nextdevelopments of products design and process risk analysisis another concept which is less studied in papers. Eventhe most optimistic people accept a percentage of risk forprocess. We should consider process risk in addition to QFDprocess and method execution because running the projectin organization needs capital. This area needs many studiesto be carried out, too. Other issue, which is less consideredin papers, is use of appropriate fuzzy number. We shouldinvestigate and find suitable kind of fuzzy numbers for eachespecial application. Most of researches have used triangularfuzzy number (TFN) because their calculations were easierthan other fuzzy numbers. Nevertheless, using TFNs mightcause losing information in some cases. Also most of studiesuse normal fuzzy numbers. In this case, appropriate fuzzynumber—normal or abnormal—should be determined. Insome cases, algebraic operations are used for working withfuzzy setswhich can lead towrong results.we should use fuzzyoperation such as x-cuts in these cases. Also, defuzzifying offuzzy numbers many lead to lose information, so we shouldtry not to defuzzify in early stages of calculations and usefuzzy operations as far as we can. Other proposed methodto deal with this issue is using lingual variables and avoidingtranslating them into fuzzy numbers [59].

Although QFD has been invented to design productaccording to customer needs, we cannot just consider CNsand forget about market competiveness. It is possible toincrease your market share even with a product lower thancustomer expectation because of market low competitive-ness and vice versa. Hence, in addition to attention tocustomer needs, one should use competition analysis todetermine strengths, weaknesses, targetmarket situation, andsale points. It should be noticed that customer preferencesmay change after introducing product to market due to

different factors [8]. So, models should be developed in away that can anticipate changes in customer needs and putthis anticipating in product design after initial introducing ofproduct. Although fuzzy linear and nonlinear programmingmodels have been used frequently in FQFD process, mostof their application is in determining of satisfaction levelof process characteristics according to existing constraints.Most of papers used linearmethods and there are a fewpapersused nonlinear models. In case of multiattribute models, onemust notice differences between AHP and ANP methods.Although fuzzy AHP is easier and less time consuming thanfuzzy ANP, but when there are interdependencies betweenparameters, one must use fuzzy ANP. Increasing applicationof FAHP in papers, in spite of interdependencies betweenTCs and CNs, is concerning and should be reviewed. Goalprogramming is one of the MCDM methods and usedwidely in FQFD process. Goal programming includes severalmethods such as zero-one goal programming and weightinggoal programming. Most of studies use zero-one goal pro-gramming; it should be investigated when we can chooseone of GP methods. Using GP in combination with othermethods is another important point. We cannot fulfill allneeds of design team with just these methods. Also, numberof employed constraints in this decision making methodsshould be selected according to company situation, kindof problem and its complexity, and market competitivenesssituation, so its results could be applied in real world. Whilemany GPmodels consider coefficients as crisp numbers, theyshould be considered as fuzzy numbers, according to fuzzyenvironment and operations. It decreases risk of informationlost.

Many of discussed papers used hybrid models. In spiteof big size of calculations, it seems that combination ofdifferent methods is a promising trend in FQFD. They canbe combined to cover weaknesses of each other and use theirstrengths. It’s better to compare results of all used methods toreach the best method. Using fuzzy group decision makingmodels is another promising trend which seems necessarybecause of several decision makers (QFD team) who wereinvolved in QFD process.

Metaheuristic algorithms are another new methodemployed in FQFD. The most frequent meta-heuristicalgorithms used in FQFD are genetic algorithm (GA) andartificial neural networks (ANN). According to increasingtrend of using these algorithms, it is expected that othermeta-heuristic algorithms will be used in QFD, too. Thesemethods were mostly used to determine relations betweencharacteristics especially in quality matrix (or HoQ). Theycan be used to calculate target values of TCs and determinefulfillment level of these characteristics.

In the case of QFD applications, QFD can be used inall industries (both manufacturing and service), but, amongpapers published in different databases and discussed herein this review, QFD was used SCM and logistics morethan other discussed applications. Using QFD to developproducts according to environmental considerations is anew, promising trend. QFD applications in designing expertsystems and decision support systems have a long way to go.In these two last cases, there is amutual relation betweenQFD


and them. We can use QFD to select and develop DSS andexpert systems; on the other side, these systems can be usedto deploy QFD’s proposed models easier to make them user-friendly by decreasing calculations size.

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