An integrated behavior decision-making approach for large group quality function deployment

Quality function deployment (QFD) is a team-based product development technique for determining critical engineering characteristics based on customer requirements to realize higher customer satisfaction. Given its simplicity and visibility, the QFD has been broadly utilized in a variety of industries for product or service planning. However, traditional QFD method assumes that the behavior of domain experts is completely rational, and is only suitable for product development problems with a small number of experts. In this paper, we aim to develop a new large group QFD approach based on an extended TODIM (Portuguese acronym for interactive multi-criteria decision making) method under interval type-2 fuzzy context. The interval type-2 fuzzy sets are utilized to describe the imprecise and uncertain correlation assessment information between customer requirements and engineering characteristics. The normal TODIM is modified and used to derive the priority of engineering characteristics considering the psychological factors of experts. Also, an entropy weighting method was adopted to derive the weights of customer requirements objectively. Finally, we provide an electric vehicle product development case and perform a comparison analysis with previous methods to reveal the effectiveness and practicability of the proposed large group QFD approach. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

Quality function deployment (QFD) is a team-based product development technique that aims to determine critical engineering characteristics based on customer requirements for higher customer satisfaction. This paper proposes a new large group QFD approach based on an extended TODIM method and interval type-2 fuzzy sets, with modifications to consider psychological factors and objective weighting methods. The effectiveness and practicability of the proposed approach are demonstrated through a comparison analysis with previous methods using an electric vehicle product development case study.