A new integrated fuzzy QFD approach for market segments evaluation and selection

Dat, Luu Quoc; Phuong, Thinh Thi; Kao, Hsing Pei; Chou, Shuo Yan; Nghia, Pham Van

doi:10.1016/j.apm.2014.11.051

Cited by 64 publications

(24 citation statements)

References 34 publications

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“…To validate the effectiveness of the proposed QFD, a comparative analysis with the conventional QFD and the fuzzy QFD [51] methods is conducted on the same problem of market segments evaluation. In addition, an extended linguistic QFD approach based on discrete numbers [52] is chosen to facilitate the comparative analysis.…”

Section: Comparisons and Discussionmentioning

confidence: 99%

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

2016

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Quality function deployment (QFD) is a widely used quality system tool for translating customer requirements (CRs) into the engineering design requirements (DRs) of products or services. The conventional QFD analysis, however, has been criticized as having some limitations such as in the assessment of relationships between CRs and DRs, the determination of CR weights and the prioritization of DRs. This paper aims to develop a new hybrid group decision-making model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX (qualitative flexible multiple criteria method) approach for handling QFD problems with incomplete weight information. First, hesitant linguistic term sets are combined with interval 2-tuple linguistic variables to express various uncertainties in the assessment information of QFD team members. Borrowing the idea of grey relational analysis (GRA), a multiple objective optimization model is constructed to determine the relative weights of CRs. Then, an extended QUALIFLEX approach with an inclusion comparison method is suggested to determine the ranking of the DRs identified in QFD. Finally, an analysis of a market segment selection problem is conducted to demonstrate and validate the proposed QFD approach.

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Section: Comparisons and Discussionmentioning

confidence: 99%

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

2016

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“…Ozgen 20084 [45] Chang and Chen (2011) 7 [47] xOvvmu}t -=D ?=NDv= Dursun and Karsak ( 2013)8 [48] p}@twD= |R=UR=@ CavY Yang; Ong etal: (2013) 9 [49] |v@r`}=vY Aya g; Samanlioglu etal:(2013) 10 [50] |DW=Oy@ |=yC@k= Qt Camg oz Akda g; Tarimetal: (2013) 11 [51] ur}D=|rB |=yxrwr Q=Wi |Q}oxR=Ov= O}OH C= R}yHD Zaim; Sevklietal: (2014) 12 [52] 16 [55] |DW=Oy@ |=yC@k= Qt Saadon; Mustafa etal: (2015) 17 [56] |DW=Oy@ |=yC@k= Qt Lee; Ru etal: (2015) 18 [57] IT |R}Qxt=vQ@ B uy uk ozkan and G ulery uz 201519 [58] Q= R=@ VN@ ?=NDv= Dat; Phuongetal: (2015) 20 [59] [39] "OvDW=O 6 wDQ=B p}rLD w QFD l} u}@nvy w nv}Hw=W [40] "Ov=xDN=OQB QFD VwQ R= xO=iDU= =@ xQ=DU GvB pDy C=k}kLD Q}N= |=yp=U QO [41] 62. Khorshidi, H.A., Nikfalazar, S. and Gunawan, I.…”

Section: Bayraktaro Glu Andmentioning

confidence: 99%

ارائه‌ی رویکردی ترکیبی از خانه‌ی کیفیت فازی، تحلیل سروکوال و کانو - مطالعه‌ی موردی: هتل پردیس شهر- رشت

نیا¹,

آزاد²

2019

مهندسی صنایع و مدیریت

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C}i}m |xv=N Ovv=t |}=yVwQ R= OQwt 31 =OD@= 'Q=DWwv u}= QO "OW=@ u=}QDWt C}=[Q w C=tON C}i}m R= |@ wr]t |xHQO u}}aD x@ w xOW |}=U=vW CWQ T}OQB pDy C=tON R= u=}QDWt |=yxDU=wN xDN=OQB |QDWt 390 u}@ \w@Qt |=yxt=vVUQB`}RwD =@ wv=m w p=wmwQU p}rLD w C}ty= u}t-=D |= Q@ sRq |LqY= C=t=Ok= w =yC}r=ai 'C}i}m |xv=N p}mWD =@ TBU "CU= xOW QO |FrFt |R=i O=Oa= |x}Q_v R= 'u}= Q@ xwqa "CU= xOW |yOuRw w |}=U=vW =yxDU=wN w`wv 'C}ty= j}kLD u}= R= xOt; CUO x@ G}=Dv "OwW|t xDiQo xQy@ j}kLD VwQ QU=QU uOQm xOQw

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“…Dat et al. () presented a QFD method based on fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method so that the final orders of alternatives are derived for the selection of appropriate market segments. Çevik Onar et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Besides, the weights of CRs are given directly in the traditional QFD, but the weight information regarding CRs may be incompletely known in practical product designs (Wang et al., ). What is more, the weighted average method is commonly used in the traditional QFD, which is not suitable for ranking ECs exactly (Dat et al., ; Wu et al., ).…”

Section: Introductionmentioning

confidence: 99%

Improving quality function deployment analysis with the cloud MULTIMOORA method

You

Liu

et al. 2017

Int Trans Operational Res

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Quality function deployment (QFD) is a quality guarantee method extensively used in various industries, which can help enterprises shorten the product design period and enhance the manufacturing and managing work. The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model.

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A new integrated fuzzy QFD approach for market segments evaluation and selection

Cited by 64 publications

References 34 publications

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

ارائه‌ی رویکردی ترکیبی از خانه‌ی کیفیت فازی، تحلیل سروکوال و کانو - مطالعه‌ی موردی: هتل پردیس شهر- رشت

Improving quality function deployment analysis with the cloud MULTIMOORA method

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