A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

Chun Kit Kwong, Y. Ye, Y. Chen, King Lun Tommy Choy

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen etal. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553-1556).
Original languageEnglish
Pages (from-to)5801-5820
Number of pages20
JournalInternational Journal of Production Research
Issue number19
Publication statusPublished - 1 Oct 2011


  • consensus ordinal ranking
  • engineering characteristics
  • fuzzy group decision-making
  • fuzzy weighted average
  • quality function deployment

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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