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A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning

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  • Pai Zheng

    (University of Auckland)

  • Xun Xu

    (University of Auckland)

  • Sheng Quan Xie

    (University of Auckland)

Abstract

Customer requirements (CRs) play a significant role in the product development process, especially in the early design stage. Quality function deployment (QFD), as a useful tool in customer-oriented product development, provides a systematic approach towards satisfying CRs. Customers are heterogeneous and their requirements are often vague, therefore, how to determine the relative importance ratings (RIRs) of CRs and eventually evaluate the final importance ratings is a critical step in the QFD product planning process. Aiming to improve the existing approaches by interpreting various CR preferences more objectively and accurately, this paper proposes a weighted interval rough number method. CRs are rated with interval numbers, rather than a crisp number, which is more flexible to adapt in real life; also, the fusion of customer heterogeneity is addressed by assigning different weights to customers based on several factors. The consistency of RIRs is maintained by the proposed procedures with design rules. A comparative study among fuzzy weighted average method, rough number method and the proposed method is conducted at last. The result shows that the proposed method is more suitable in determining the RIRs of CRs with vague information.

Suggested Citation

  • Pai Zheng & Xun Xu & Sheng Quan Xie, 2019. "A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 3-16, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1224-z
    DOI: 10.1007/s10845-016-1224-z
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    References listed on IDEAS

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    Cited by:

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    6. Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    7. Jafarian, Ahmad & Rabiee, Meysam & Tavana, Madjid, 2020. "A novel multi-objective co-evolutionary approach for supply chain gap analysis with consideration of uncertainties," International Journal of Production Economics, Elsevier, vol. 228(C).

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