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Article type: Research Article
Authors: Zhu, Yana | Fan, Chuanhaoa | Xiao, Jingb; * | Liu, Shenghuaa
Affiliations: [a] Business School, Hohai University, Nanjing, China | [b] School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
Correspondence: [*] Corresponding author. Jing Xiao, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China. E-mail: xiaojing@njust.edu.cn.
Abstract: Quality function deployment (QFD) is a quality management tool that aims to improve customer satisfaction by transforming customer requirements into technical characteristics. It is a crucial procedure to obtain the prioritization of technical characteristics for the products or services in QFD. Traditional QFDs are often implemented by a small number of QFD members. However, with the increasing complexity of product and service design, QFD requires the participation of more QFD members from dispersed departments or institutions. Additionally, the evaluation information given by QFD members may widely differ due to their different knowledge and background. Furthermore, the psychological behaviours of QFD members also greatly influence the final prioritization of technical characteristics. Hence, this paper proposes a novel QFD framework to prioritize technical characteristics using a consensus-reaching process and prospect theory when large numbers of QFD members are involved. In the large-scale QFD framework, prospect theory is generally utilized to depict the psychological behaviours of QFD members. Then, QFD members are divided into several clusters. Eventually, a consensus-reaching process is established to assist QFD members in reaching a consensus. To verify the practicability of the presented framework, this paper applies it to the evaluation of contingency plan to determine the critical measures.
Keywords: Quality function deployment, consensus-reaching process, prospect theory, large-scale group decision making, contingency plan
DOI: 10.3233/JIFS-202326
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 575-594, 2021
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