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Engineering Characteristics Prioritization in Quality Function Deployment Using an Improved ORESTE Method with Double Hierarchy Hesitant Linguistic Information

Author

Listed:
  • Hua Shi

    (School of Materials, Shanghai Dianji University, Shanghai 201306, China)

  • Ling-Xiang Mao

    (School of Economics and Management, Anhui Normal University, Wuhu 241002, China)

  • Ke Li

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Xiang-Hu Wang

    (School of Materials, Shanghai Dianji University, Shanghai 201306, China)

  • Hu-Chen Liu

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

Quality function deployment (QFD) is a customer-driven product development technique widely utilized to translating customer requirements into engineering characteristics for maximum customer satisfaction. Nonetheless, when used in real situations, the traditional QFD method has been criticized to have many deficiencies, e.g., in expressing experts’ uncertain assessments and prioritizing engineering characteristics. In this study, we propose a new engineering characteristics prioritization approach based on double hierarchy hesitant linguistic term sets (DHHLTSs) and the ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method to overcome the shortcomings of the traditional QFD. Specifically, the main contributions of this study to the literature are that the DHHLTSs are utilized to describe the hesitant relationship assessments between customer requirements and engineering characteristics provided by experts, and the ORESTE method is modified and used to determine the importance ranking orders of engineering characteristics. Finally, a case study and a comparison analysis are presented to illustrate the feasibility and practicability of the proposed QFD approach. The advantages of the new approach being proposed are higher flexibility in handling experts’ intricate and hesitant relationship evaluation information and effective in providing a reasonable prioritization of engineering characteristics in the practical QFD analysis.

Suggested Citation

  • Hua Shi & Ling-Xiang Mao & Ke Li & Xiang-Hu Wang & Hu-Chen Liu, 2022. "Engineering Characteristics Prioritization in Quality Function Deployment Using an Improved ORESTE Method with Double Hierarchy Hesitant Linguistic Information," Sustainability, MDPI, vol. 14(15), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9771-:d:883059
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    References listed on IDEAS

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    1. Jia Huang & Ling-Xiang Mao & Hu-Chen Liu & Min-shun Song, 2022. "Quality function deployment improvement: A bibliometric analysis and literature review," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1347-1366, June.
    2. Ye-Jia Ping & Ran Liu & Ze-Ling Wang & Hu-Chen Liu, 2022. "New approach for quality function deployment with an extended alternative queuing method under linguistic Pythagorean fuzzy environment," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 16(3), pages 349-370.
    3. Song-Man Wu & Hu-Chen Liu & Li-En Wang, 2017. "Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4436-4449, August.
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    6. Jia Huang & Xiao-Yue You & Hu-Chen Liu & Sheng-Li Si, 2019. "New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory," International Journal of Production Research, Taylor & Francis Journals, vol. 57(5), pages 1283-1299, March.
    7. Keyou Shi & Yong Liu & Weizhang Liang, 2022. "An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    8. Xu Wang & Hong Fang & Wenyan Song, 2020. "Technical attribute prioritisation in QFD based on cloud model and grey relational analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 58(19), pages 5751-5768, October.
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