Product design and selection using fuzzy QFD and fuzzy MCDM approaches. (English) Zbl 1202.90184

Summary: Quality function deployment (QFD) is a useful analyzing tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have applied the fuzzy set theory to QFD and developed various fuzzy QFD models. Three issues are investigated by examining their models. First, the extant studies focused on identifying important engineering characteristics and seldom explored the subsequent prototype product selection issue. Secondly, the previous studies usually use fuzzy number algebraic operations to calculate the fuzzy sets in QFD. This approach may cause a great deviation in the result from the correct value. Thirdly, few studies have paid attention to the competitive analysis in QFD. However, it can provide product developers with a large amount of valuable information. Aimed at these three issues, this study integrates fuzzy QFD and the prototype product selection model to develop a product design and selection (PDS) approach. In fuzzy QFD, the \(\alpha\)-cut operation is adopted to calculate the fuzzy set of each component. Competitive analysis and the correlations among engineering characteristics are also considered. In prototype product selection, engineering characteristics and the factors involved in product development are considered. A fuzzy multi-criteria decision making (MCDM) approach is proposed to select the best prototype product. A case study is given to illustrate the research steps for the proposed PDS method. The proposed method provides product developers with more useful information and precise analysis results. Thus, the PDS method can serve as a helpful decision-aid tool in product design.


90B90 Case-oriented studies in operations research
90B50 Management decision making, including multiple objectives
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
Full Text: DOI


[1] Wasserman, G.S., On how prioritize design requirements during the QFD planning process, IIE trans., 25, 59-65, (1993)
[2] Lockamy, A.; Khurana, A., Quality function deployment: total quality management for new product design, Int. J. qual. reliab. manage., 12, 73-84, (1995)
[3] Khoo, L.P.; Ho, N.C., Framework of a fuzzy quality function deployment system, Int. J. prod. res., 34, 299-311, (1996) · Zbl 0924.90083
[4] Shen, X.X.; Tan, K.C.; Xie, M., The implementation of quality function deployment based on linguistic data, J. intell. manuf., 12, 65-75, (2001)
[5] Bottani, E.; Rizzi, A., Strategic management of logistics service: a fuzzy QFD approach, Int. J. prod. econ., 103, 585-599, (2006)
[6] Wang, J., Fuzzy outranking approach to prioritize design requirements in quality function deployment, Int. J. prod. res., 37, 899-916, (1999) · Zbl 0940.90515
[7] Chan, L.K.; Kao, H.P.; Ng, A.; Wu, M.L., Rating the importance of customer needs in quality function deployment by fuzzy and entropy methods, Int. J. prod. res., 37, 2499-2518, (1999) · Zbl 0949.90564
[8] Shen, X.X.; Min, X.; Tan, K.C., Listening to the future voice of the customer using fuzzy trend analysis in quality function deployment, Qual. eng., 13, 419-425, (2001)
[9] Sohn, Y.S.; Choi, I.S., Fuzzy QFD for supply chain management with reliability, Reliab. eng. syst. safe., 72, 327-334, (2001)
[10] Tsai, C.Y., Using fuzzy QFD to enhance manufacturing strategic planning, J. chin. inst. ind. eng., 18, 33-41, (2003)
[11] Büyüközkan, G.; Feyzioğlu, O.; Ruan, D., Fuzzy group decision-making to multiple preference formats in quality function deployment, Comput. ind., 58, 392-402, (2004)
[12] Ertay, T.; Büyüközkan, G.; Kahraman, C.; Ruan, D., Quality function deployment implementation based on analytic network process with linguistic data: an application in automotive industry, J. intell. fuzzy syst., 16, 221-232, (2005)
[13] Kahraman, C.; Ertay, T.; Büyüközkan, G., A fuzzy optimization model for QFD planning process using analytic network approach, Eur. J. oper. res., 171, 390-411, (2006) · Zbl 1090.90016
[14] Chen, Y.; Fung, R.Y.K.; Tang, J., Fuzzy expected value modeling approach for determining target values of engineering characteristics in QFD, Int. J. prod. res., 43, 3583-3604, (2005) · Zbl 1082.90517
[15] Chen, Y.; Fung, R.Y.K.; Tang, J., Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator, Eur. J. oper. res., 174, 1553-1566, (2006) · Zbl 1103.90329
[16] Chen, L.S.; Weng, M.C., An evaluation approach to engineering design in QFD processes using fuzzy goal programming models, Eur. J. oper. res., 172, 230-248, (2006) · Zbl 1116.90067
[17] Kwong, C.K.; Chen, Y.; Bai, H.; Chan, D.S.K., A methodology of determining aggregated importance of engineering characteristics in QFD, Comput. ind. eng., 53, 667-679, (2007)
[18] Vanegas, L.V.; Labib, A.W., A fuzzy quality function deployment model for deriving optimum targets, Int. J. prod. res., 39, 99-120, (2001) · Zbl 0976.90500
[19] Lin, C.T., A fuzzy logic-based approach for implementing quality function deployment, Int. J. smart eng. syst. des., 5, 55-62, (2003)
[20] Hauser, J.R.; Clausing, D., The house of quality, Harvard business rev., 63-73, (1988)
[21] Cohen, L., Quality function deployment – how to make QFD work for you?, (1995), Addison-Wesley Publishing New York
[22] Revelle, J.B.; Moran, J.W.; Cox, C.A., The QFD handbook, (1998), John Wiley & Sons New York
[23] Suh, N.P., The principles of design, (1990), Oxford University Press New York
[24] Cochran, D.S.; Arinez, J.A.; Duda, W.D.; Linck, J., A decomposition approach for manufacturing system design, J. manuf. syst., 20, 371-389, (2001)
[25] Kulak; Durmusoglu, M.B.; Tufekci, S., A complete cellular manufacturing system design methodology based on axiomatic design principles, Comput. ind. eng., 48, 765-787, (2005)
[26] A.K. Kar, Linking axiomatic design and Taguchi methods via information content in design, in: First International Conference on Axiomatic Design, Cambridge, 2000.
[27] Chen, K.Z., Development of integrated design for disassembly and recycling in concurrent engineering, Integ. manuf. syst., 12, 67-79, (2001)
[28] Huang, G.Q.; Jiang, Z., Web-based design review of fuel pumps using fuzzy set theory, Eng. appl. artif. intel., 15, 529-539, (2002)
[29] Goncalves-Coelho, A.M.; Mourao, A.J.F.; Pereira, Z.L., Improving the use of QFD with axiomatic design, Concurrent eng – res. appl., 13, 233-239, (2005)
[30] EL-Haik, B.S., Axiomatic quality: integrating axiomatic design with six-sigma, reliability, and quality engineering, (2005), John Wiley & Sons, Inc. New Jersey · Zbl 1131.90019
[31] Ng, N.K.; Jiao, J., A domain-based reference model for the conceptualization of factory loading allocation problems in multi-site manufacturing supply chains, Technovation, 24, 631-642, (2004)
[32] Kulak, A decision support system for fuzzy multi-attribute selection of material handling equipments, Expert syst. appl., 29, 310-319, (2005)
[33] Celik, M.; Cebi, S.; Kahraman, C.; Er, I.D., Application of axiomatic design and TOPSIS methodologies under fuzzy environment for proposing competitive strategies on turkish container ports in maritime transportation network, Expert syst. appl., 36, 4541-4557, (2009)
[34] Celik, M.; Cebi, S.; Kahraman, C.; Er, I.D., An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market, Expert syst. appl., 36, 6227-6235, (2009)
[35] Kahraman, C.; Cebi, S., A new multi-attribute decision making method: hierarchical fuzzy axiomatic design, Expert syst. appl., 36, 4848-4861, (2009)
[36] Dong, W.; Shah, H.; Wong, F., Fuzzy computations in risk and decision analysis, Civil eng. syst., 2, 201-208, (1985)
[37] Chen, L.H.; Lu, H.W., An approximate approach for ranking fuzzy numbers based on left and right dominance, Comput. math. appl., 41, 1589-1602, (2001) · Zbl 0984.03041
[38] Rommelfanger, H., Interactive decision making in fuzzy linear optimization problems, Eur. J. oper. res., 41, 210-217, (1989) · Zbl 0672.90103
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