Enhancing Quality Function Deployment Through the Integration of Rough Set and Ordinal Priority Approach: A Case Study in Electric Vehicle Manufacturing

Quality function deployment (QFD) is a widely used technique for translating customer requirements (CRs) into engineering characteristics (ECs) in product or service design. However, existing improved QFD methods suffer from several limitations, such as relying on precise experts' assessments, subjectivity in aggregating evaluation information, and excessive external information and parameters, which may increase the the complexity of QFD and hinder its practical application. To address these challenges, this article presents a novel rough set theory-based ordinal priority approach (OPA) methodology (OPA-R) to enhance traditional QFD. The proposed approach uses ordinal priorities provided by experts to evaluate CRs and the interrelations between CRs and ECs, eliminating the need for fuzzy linguistic variables, fuzzy numbers, or pairwise comparison matrices. Moreover, rough set theory is employed to aggregate the assessments of experts to generate rough ordinal priorities. An extended optimization model of traditional OPA is then developed to determine the ranking of CRs and ECs. The validity and advantages of the proposed model are demonstrated through a case study in the manufacturing of electric vehicles. The OPA-R method can simplify the process of QFD, reduce the reliance on precise assessments from experts, and avoid excessive external information and additional parameters.

Automated summary

This article presents a novel rough set theory-based ordinal priority approach (OPA-R) methodology to enhance traditional Quality Function Deployment (QFD) by eliminating the need for fuzzy linguistic variables and pairwise comparison matrices. The proposed methodology uses experts' ordinal priorities to evaluate customer requirements and the interrelations between requirements and engineering characteristics. The validity and advantages of the proposed model are demonstrated through a case study in the manufacturing of electric vehicles.