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Article type: Research Article
Authors: Yang, Qianga; b | Li, Yan-Laia; b; * | Chin, Kwai-Sangc
Affiliations: [a] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, People’s Republic of China | [b] National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan, People’s Republic of China | [c] Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong, People’s Republic of China
Correspondence: [*] Corresponding author. Yan-Lai Li, E-mail: [email protected].
Abstract: Quality function deployment (QFD) is an effective tool for the design and improvement of products/services. The prioritization of customer requirements (CRs), as an essential component of the house of quality, is fundamental and strategic in the whole process of QFD product planning. This study proposes a novel ordinal scale values based group decision-making (GDM) approach first and it is subsequently used to prioritize CRs in QFD product planning. The proposed approach is composed of three stages, that is, constructing the integrated preference vector, defining the extraction sequence and constructing the comprehensive preference vector. The proposed GDM approach is good for utilizing the ordinal scale values provided by respondents due to limited experience and knowledge. An illustrative example is presented to verify the applicability and efficiency of the proposed approach. Further, to demonstrate the superiority of the proposed approach, comparisons are made between the proposed approach and two other similar methods. Practical results demonstrated that the proposed approach can be effective when the importance of customers and the preference evaluations of CRs are given by an ordinal scale.
Keywords: Quality function deployment (QFD), customer requirement (CR), group decision-making (GDM), ordinal scale, preference ordering
DOI: 10.3233/JIFS-190444
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4349-4367, 2019
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