New Approach for Quality Function Deployment Using Linguistic Z-Numbers and EDAS Method

Mao, Ling-Xiang; Liu, Ran; Mou, Xun; Liu, Hu-Chen

doi:10.15388/21-INFOR455

Informatica

New Approach for Quality Function Deployment Using Linguistic Z-Numbers and EDAS Method

Volume 32, Issue 3 (2021), pp. 565–582

Ling-Xiang Mao Ran Liu Xun Mou Hu-Chen Liu

https://doi.org/10.15388/21-INFOR455

Pub. online: 8 July 2021 Type: Research Article

Open Access

Received
1 July 2020

Accepted
1 June 2021

Published
8 July 2021

Abstract

Quality function deployment (QFD) is an effective product development and management tool, which has been broadly applied in various industries to develop and improve products or services. Nonetheless, when used in real situations, the traditional QFD method shows some important weaknesses, especially in describing experts’ opinions, weighting customer requirements, and ranking engineering characteristics. In this study, a new QFD approach integrating linguistic Z-numbers and evaluation based on distance from average solution (EDAS) method is proposed to determine the prioritization of engineering characteristics. Specially, linguistic Z-numbers are adopted to deal with the vague evaluation information provided by experts on the relationships among customer requirements and engineering characteristics. Then, the EDAS method is extended to estimate the final priority ratings of engineering characteristics. Additionally, stepwise weight assessment ratio analysis (SWARA) method is employed to derive the relative weights of customer requirements. Finally, a practical case of Panda shared car design is introduced and a comparison is conducted to verify the feasibility and effectiveness of the proposed QFD approach. The results show that the proposed linguistic Z-EDAS method can not only represent experts’ interrelation evaluation information flexibly, but also produce a more reasonable and reliable prioritization of engineering characteristics in QFD.

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Biographies

Mao Ling-Xiang

L.-X. Mao received the BS degree in industrial engineering from Shanghai Maritime University, China, in 2006, the MS degree in industrial engineering from Tongji University, China, in 2010, and the PhD degree in information resource management from Nanjing University, China, in 2014. He is working as a postdoctoral research fellow at the School of Management, Shanghai University. His research interests include quality function deployment and information product management.

Liu Ran

R. Liu received the MSc degree in logistics engineering from Shanghai University, Shanghai, China, in 2017. She is currently pursuing the PhD degree in management science and engineering at the School of Management, Shanghai University, Shanghai, China. Her research interests include occupational health and safety management, fault detection and diagnosis, and quality management.

Mou Xun

xunmou@foxmail.com

X. Mou received the BS degree in industrial engineering from Qingdao University, Qingdao, China, in 2019. She is currently working toward the MS degree in management science and engineering at the School of Management, Shanghai University, Shanghai, China. Her research interests include artificial intelligence, and Petri net theory and applications.

Liu Hu-Chen

huchenliu@tongji.edu.cn

H.-C. Liu is a professor at the School of Economics and Management, Tongji University. His main research interests include quality and reliability management, artificial intelligence, and Petri net theory and application. He has published three books and 100+ papers in these areas in leading journals, such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Engineering Management, Automation in Construction, Reliability Engineering & System Safety, International Journal of Production Research, and Annals of Operations Research. Dr. Liu is an Associate Academician of the International Academy for Quality, and is a Senior member of the IEEE Reliability Society, the Institute of Industrial & Systems Engineers (IISE), and the China Association for Quality.

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Keywords

quality function deployment linguistic Z-number evaluation based on distance from average solution (EDAS) SWARA method product development

Funding

This study was supported by the Humanities and Social Sciences Research Project for Universities of Anhui China (No. SK2019A0267) and the Fundamental Research Funds for the Central Universities (No. 22120210080).

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