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Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis

Author

Listed:
  • Majid Baghery

    (Allameh Tabatabai University)

  • Samuel Yousefi

    (Urmia University of Technology)

  • Mustafa Jahangoshai Rezaee

    (Urmia University of Technology)

Abstract

Nowadays, decision-making process is faced with different challenges. There are many aspects that must be considered and planned, especially programs in the automotive industry that is associated with human vital factors are not structured. Therefore, managerial and engineering techniques should be used to solve the existing problems. In this regard, the process failure mode and effects nalysis (PFMEA) technique is one of the ways to assess the potential product or process failures and their effects, designs from the beginning to the end of the product life cycle, and identifies actions to eliminate the failures or reduce their effects. The well-known method for piriortizing these failures is risk priority number (RPN). Because the RPN has problems in prioritization of critical processes, a new approach is needed to remove these problems.Thus, in a real case study, first PFMEA technique has been implemented with the help of multidisciplinary teams for the parts of Peugeot 206, Peugeot 405 and Samand (three types of automobiles produced by Iran-khodro company) and affected factors have been obtained as an interval. Then, interval data envelopment analysis (DEA) have been used to prioritize and analyze all failures that are identified by the PFMEA technique for every part. Finally, by combining the results of the interval DEA and Grey relational analysis (GRA), the manufacturing processes are prioritized based on their criticality. Moreover, the proposed actions for all the items associated with each process are provided to prevent some potential failures. The results show that pouring and core making are the most crucial processes in this study, respectively. According to the proposed approach, the decision makers may determine critical processes and plan and do appropriate actions for removing failures or reducing their effects.

Suggested Citation

  • Majid Baghery & Samuel Yousefi & Mustafa Jahangoshai Rezaee, 2018. "Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1803-1825, December.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1214-1
    DOI: 10.1007/s10845-016-1214-1
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    References listed on IDEAS

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    1. Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Lin, Sue J. & Lu, I.J. & Lewis, Charles, 2007. "Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan," Energy Policy, Elsevier, vol. 35(3), pages 1948-1955, March.
    4. Despotis, Dimitris K. & Smirlis, Yiannis G., 2002. "Data envelopment analysis with imprecise data," European Journal of Operational Research, Elsevier, vol. 140(1), pages 24-36, July.
    5. Emrouznejad, Ali & De Witte, Kristof, 2010. "COOPER-framework: A unified process for non-parametric projects," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1573-1586, December.
    6. Emrouznejad, A. & De Witte, K., 2010. "COOPER-framework: A Unified Standard Process for Non-parametric Projects," Working Papers 18, Top Institute for Evidence Based Education Research.
    7. He, Zhen & Zhu, Peng-Fei & Park, Sung-Hyun, 2012. "A robust desirability function method for multi-response surface optimization considering model uncertainty," European Journal of Operational Research, Elsevier, vol. 221(1), pages 241-247.
    8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    9. Jenkins, Larry & Anderson, Murray, 2003. "A multivariate statistical approach to reducing the number of variables in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 51-61, May.
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    Cited by:

    1. Jia Huang & Hu-Chen Liu & Chun-Yan Duan & Ming-Shun Song, 2022. "An improved reliability model for FMEA using probabilistic linguistic term sets and TODIM method," Annals of Operations Research, Springer, vol. 312(1), pages 235-258, May.
    2. Jing Xiao & Xiuli Wang & Hengjie Zhang, 2022. "Exploring the Ordinal Classifications of Failure Modes in the Reliability Management: An Optimization-Based Consensus Model with Bounded Confidences," Group Decision and Negotiation, Springer, vol. 31(1), pages 49-80, February.
    3. Xinlong Li & Yan Ran & Genbao Zhang & Yan He, 2020. "A failure mode and risk assessment method based on cloud model," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1339-1352, August.
    4. Wang, Qun & Jia, Guozhu & Jia, Yuning & Song, Wenyan, 2021. "A new approach for risk assessment of failure modes considering risk interaction and propagation effects," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Jahangoshai Rezaee, Mustafa & Yousefi, Samuel & Hayati, Jamileh, 2019. "Root barriers management in development of renewable energy resources in Iran: An interpretative structural modeling approach," Energy Policy, Elsevier, vol. 129(C), pages 292-306.

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