A large-scale group decision-making approach for quality function deployment based on Dempster-Shafer evidence theory and hierarchical clustering algorithm

Quality Function Deployment (QFD) is a classic customer requirements (CRs)-oriented quality management method. However, the increasing complexity and diversity of CRs in the modern society makes it impossible for the traditional QFD approach with a limited number of team members (TMs) to fully satisfy CRs. Therefore, in order to solve the QFD problem in complex environments, this paper proposes an improved QFD method based on Dempster–Shafer evidence theory (D-S theory) and hierarchical clustering algorithm in large-scale group environments. Firstly, utilizing the advantages of D-S theory in information processing and synthesis, the evaluation of quality characteristics (QCs) in the form of probabilistic linguistic term sets (PLTSs) is transformed into basic probability assignments (BPAs) to handle uncertainty more flexibly. Secondly, this paper designs a hierarchical clustering algorithm based on bounded confidence to divide TMs into subgroups, and fully considers the interaction willingness of TMs during the clustering process to ensure the efficiency and accuracy of decision-making. On this basis, the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on distance degree is introduced to calculate the weight of CRs in a more objective way. Then, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method based on D-S theory is used to deeply analyze the mutual influence relationship between QCs to reveal its internal logic. Besides, combined with the psychological expectations of TMs, the disappointment theory is used to prioritize QCs to ensure that products or services are more in line with customer expectations. Finally, this paper applies the proposed method to the development process of mobile health applications (mHealth apps) from the perspective of privacy security, verifying the practicability and superiority of the method. The effectiveness of the method in CRs transformation and product design optimization is further demonstrated through parametric and comparative analyses.