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A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment

Author

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  • Tai-Wu Chang

    (Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan)

  • Huai-Wei Lo

    (Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan)

  • Kai-Ying Chen

    (Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan)

  • James J. H. Liou

    (Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan)

Abstract

Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products.

Suggested Citation

  • Tai-Wu Chang & Huai-Wei Lo & Kai-Ying Chen & James J. H. Liou, 2019. "A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:874-:d:269144
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    References listed on IDEAS

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    1. Željko Stević & Dragan Pamučar & Marko Subotić & Jurgita Antuchevičiene & Edmundas Kazimieras Zavadskas, 2018. "The Location Selection for Roundabout Construction Using Rough BWM-Rough WASPAS Approach Based on a New Rough Hamy Aggregator," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    2. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching, 2019. "A novel failure mode and effect analysis model for machine tool risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 173-183.
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    Cited by:

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    2. Shih-Ping Shen & Jung-Fa Tsai, 2022. "Evaluating the Sustainable Development of the Semiconductor Industry Using BWM and Fuzzy TOPSIS," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    3. Peace Y. L. Liu & James J. H. Liou & Sun-Weng Huang, 2023. "Exploring the Barriers to the Advancement of 3D Printing Technology," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    4. Jen-Jen Yang & Yen-Ching Chuang & Huai-Wei Lo & Ting-I Lee, 2020. "A Two-Stage MCDM Model for Exploring the Influential Relationships of Sustainable Sports Tourism Criteria in Taichung City," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    5. Ferenc Bognár & Csaba Hegedűs, 2022. "Analysis and Consequences on Some Aggregation Functions of PRISM (Partial Risk Map) Risk Assessment Method," Mathematics, MDPI, vol. 10(5), pages 1-19, February.

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