A New Method for Quality Function Deployment Based on Rough Cloud Model Theory

Quality function deployment (QFD) has been a popular product or service development method to convert customer needs (CNs) into technical requirements (TRs). Nevertheless, the traditional QFD has vagueness inherent in experts' opinions in determining CNs' weights and the relationship between CNs and TRs. In the previous research works, fuzzy set theory is popular to deal with imprecise information in QFD. However, it still suffers from several deficiencies. For example, it needs prior information that leads to relatively fixed intervals expressing vagueness; and it assumes the membership degrees are crisp. To solve the issues, this article develops an integrated QFD method, where rough sets and cloud model are utilized for treating uncertain information. The former expresses impreciseness without any other assumptions and the latter considers expert evaluations' randomness. In order to obtain more comprehensive importance of CNs, a combination weighting method is utilized. In the end, a compressor rotor's industrial service design is conducted utilizing the proposed method, where its effectiveness is verified.

Automated summary

Quality Function Deployment (QFD) is a popular method for converting customer needs into technical requirements. However, traditional QFD suffers from the vagueness inherent in experts' opinions when determining the weights of customer needs and the relationships between customer needs and technical requirements. This article proposes an integrated QFD method that utilizes rough sets and cloud model to handle uncertain information, as well as a combination weighting method to obtain a more comprehensive importance of customer needs.