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Strategic Part Prioritization for Quality Improvement Practice Using a Hybrid MCDM Framework: A Case Application in an Auto Factory

Author

Listed:
  • Fuli Zhou

    (Department of Industrial Engineering, Chongqing University, Chongqing 400044, China)

  • Xu Wang

    (Department of Industrial Engineering, Chongqing University, Chongqing 400044, China
    State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China)

  • Yun Lin

    (Department of Industrial Engineering, Chongqing University, Chongqing 400044, China
    State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China)

  • Yandong He

    (Department of Industrial Engineering, Chongqing University, Chongqing 400044, China)

  • Lin Zhou

    (Department of Industrial Engineering, Chongqing University, Chongqing 400044, China
    Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Via Venezia, Cesena 52-47521, Italy)

Abstract

Quality improvement practice (QIP), as a competitive strategy, is increasingly vital for auto factories to improve the product quality and brand reputation. Quality activity on selected automotive parts among a variety of competing candidates is featured by prioritization calculation. It arouses our interest how to select the appropriate auto part to perform quality improvement action based on the collected data from the after-sale source. Managers usually select the QIP part by the rule of thumb that is based on the quantitative criterion or the subjective preference of individuals. The total quality management (TQM) philosophy requires multiple stakeholders’ involvement, regarded as a multi-criteria decision making (MCDM) issue. This paper proposes a novel hybrid MCDM framework to select the best quality improvement solution combining the subjective and objective information. The rough set-based attribute reduction (RSAR) technique was employed to establish the hierarchy structure of influential criteria, and the decision information was collected with triangular fuzzy numbers (TFNs) for its vagueness and ambiguity. In addition, the novel hybrid MCDM framework integrating fuzzy DEMATEL (decision making trial and evaluation laboratory) method, the anti-entropy weighting (AEW) technique and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was developed to rank the alternatives with the combined weight of criteria. The results argue that the optimal solution keeps a high conformance with Shemshadi’s and Chaghooshi’s methods, which is better than the existing determination. Besides, the result analysis shows the robustness and flexibility of the proposed hybrid MCDM framework.

Suggested Citation

  • Fuli Zhou & Xu Wang & Yun Lin & Yandong He & Lin Zhou, 2016. "Strategic Part Prioritization for Quality Improvement Practice Using a Hybrid MCDM Framework: A Case Application in an Auto Factory," Sustainability, MDPI, vol. 8(6), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:6:p:559-:d:72164
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    References listed on IDEAS

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    1. D.N. Prabhakar Murthy & Wallace R. Blischke, 2006. "Warranty Management and Product Manufacture," Springer Series in Reliability Engineering, Springer, number 978-1-84628-258-4, December.
    2. Huiru Zhao & Sen Guo, 2015. "External Benefit Evaluation of Renewable Energy Power in China for Sustainability," Sustainability, MDPI, vol. 7(5), pages 1-23, April.
    3. Miguel F. Salvado & Susana G. Azevedo & João C. O. Matias & Luís M. Ferreira, 2015. "Proposal of a Sustainability Index for the Automotive Industry," Sustainability, MDPI, vol. 7(2), pages 1-32, February.
    4. Mirhedayatian, Seyed Mostafa & Azadi, Majid & Farzipoor Saen, Reza, 2014. "A novel network data envelopment analysis model for evaluating green supply chain management," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 544-554.
    5. Murthy, D. N. P. & Djamaludin, I., 2002. "New product warranty: A literature review," International Journal of Production Economics, Elsevier, vol. 79(3), pages 231-260, October.
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    2. Chen Liang & Dongshi Sun & Danlan Xie, 2023. "Identifying Waste Supply Chain Coordination Barriers with Fuzzy MCDM," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    3. Srđan Dimić & Dragan Pamučar & Srđan Ljubojević & Boban Đorović, 2016. "Strategic Transport Management Models—The Case Study of an Oil Industry," Sustainability, MDPI, vol. 8(9), pages 1-27, September.

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