A spectral clustering method to improve importance rating accuracy of customer requirements in QFD

Abstract

Quality function deployment (QFD) is a method commonly used in manufacturing industries to identify customer requirements (CRs) and the manufacturing capacity to meet CRs. Importance rate (IR) of CRs plays an important role in the QFD method. The existing methods in determining IR of CRs cannot consider all related factors of customer satisfaction, need importance, personal information, and relationship between customer satisfaction and function implementation. This paper proposes a method to improve the IR accuracy. Based on comments of customers for a product, importance rates of CRs are defined using integrated importance-performance analysis (IPA) and Kano models. IPA and Kano models are integrated by spectral clustering where a similarity matrix W is formed to balance the influence proportion between the IPA and Kano models considering comments of different customers for the product. IR of CRs is used in the QFD method to define functions and structures of the product. The proposed method is compared with several existing methods in case studies of designing an upper limb rehabilitation device and a feed drive system of the CNC machine. Results show that the proposed method has improved accuracy of IR of CRs for the product design and manufacturing.