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Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD

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Listed:
  • Weiqiang Jia
  • Zhenyu Liu
  • Zhiyun Lin
  • Chan Qiu
  • Jianrong Tan

Abstract

Quantification for the importance degree of engineering characteristics (ECs) is an essential problem in quality function deployment. In real-world scenario, it is sometimes difficult to directly evaluate the correlation degree between ECs and customer requirements (CRs) as ECs are too abstract. Thus, the target ECs have to be further decomposed into several more detailed basic ECs and organised by a multi-level hierarchical structure. The paper investigates the quantification problem for the importance degree of such target ECs and tackles two critical issues. The first issue is how to deal with the uncertainties including fuzziness and incompleteness involved during the evaluation process. A fuzzy evidential reasoning algorithm-based approach is proposed to tackle this issue and derive the correlation degree between each of the basic ECs and the whole CRs. The second issue is how to deal with the interactions among the basic ECs decomposed from the same target EC during the aggregation process. A λ -fuzzy measure and fuzzy discrete Choquet integral-based approach is proposed to tackle this issue and aggregate these basic ECs. Final importance degree of the target ECs can then be obtained. At the end of this paper, a case study is presented to verify the feasibility and effectiveness of the method we propose.

Suggested Citation

  • Weiqiang Jia & Zhenyu Liu & Zhiyun Lin & Chan Qiu & Jianrong Tan, 2016. "Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1627-1649, March.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:6:p:1627-1649
    DOI: 10.1080/00207543.2015.1041574
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    References listed on IDEAS

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    1. Chan, Lai-Kow & Wu, Ming-Lu, 2002. "Quality function deployment: A literature review," European Journal of Operational Research, Elsevier, vol. 143(3), pages 463-497, December.
    2. Chen, Liang-Hsuan & Weng, Ming-Chu, 2006. "An evaluation approach to engineering design in QFD processes using fuzzy goal programming models," European Journal of Operational Research, Elsevier, vol. 172(1), pages 230-248, July.
    3. Chen, Yizeng & Fung, Richard Y.K. & Tang, Jiafu, 2006. "Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1553-1566, November.
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    Cited by:

    1. Chao Song & Jian-Qiang Wang & Jun-Bo Li, 2020. "New Framework for Quality Function Deployment Using Linguistic Z-Numbers," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    2. Hongzhan Ma & Xuening Chu & Deyi Xue & Dongping Chen, 2019. "Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 623-639, February.
    3. 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).

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