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Fuzzy failure mode and effect analysis model for operational supply chain risks assessment: an application in canned tuna manufacturer in Thailand

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: This study proposes a multi-criterion decision-making (MCDM) framework for operational supply chain risks assessment based on fuzzy failure mode effect analysis model. The proposed framework attempts to overcome some weaknesses and disadvantages of the traditional FMEA in many aspects such as (i) considering “degree of difficulty to eliminate risks” in the assessment process, (ii) using MCDM ranking methodology instead of a risk priority number, (iii) taking both subjective and objective weights of risk criteria into account. Application of the proposed framework used canned tuna production in Thailand as a case study. Methods: In this study, the operational supply chain risks assessment is treated as fuzzy MCDM problem. Subjective weights of risk criteria are determined by experts’ judgements. Objective weights are derived by Shannon entropy method. VIKOR approach is employed to prioritize the failure modes. A sensitivity analysis is performed to examine the robustness of the proposed framework. Results and conclusions: The findings from this study indicates that the most three critical FMs are “risk of product deterioration” followed by “risk of volatility raw materials supplied” and “risk of variabilities in production processes”, respectively. It recommends that the practitioners in canned tuna industry should give the priority to mitigate these risks. Although the present study focuses on canned tuna industry, the other similar industries can apply this proposed framework to assess their operational supply chain risks in the same way.
Czasopismo
Rocznik
Strony
77--96
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • The Cluster of Logistics and Rail Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
  • The Cluster of Logistics and Rail Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
Bibliografia
  • 1. Butdee S., Phuangsalee P., 2019. Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP, Procedia Manufacturing, 30, 663-670. https://doi.org/10.1016/j.promfg.2019.02.094
  • 2. da Silva C., Barbosa-P𝑜́voa A.P., Carvalho A., 2020. Environmental monetization and risk assessment in supply chain design and planning, Journal of Cleaner Production, 270, 121552. https://doi.org/10.1016/j.jclepro.2020.121552
  • 3. Fan H., Li G., Sun H., Cheng T.C.E., 2016. An information processing perspective on supply chain risk management: antecedents, mechanism, and consequences, International Journal of Production Economics, 185, 63-75. http://doi.org/10.1016/j.ijpe.2016.11.015
  • 4. Fattahi R., Khalilzadeh M., 2018. Risk evaluation using a novel hybrid method based on FMEA extended MULTIMOORA and AHP methods under fuzzy environment, Safety Science, 102, 290-300. http://doi.org/10.1016/j.ssci.2017.10.018
  • 5. Heckmann I., Comes T., Nickel S., 2015. A Critical Review on Supply Chain Risk - Definition, Measure and Modeling, Omega, 52, 119-132. https://doi.org/10.1016/j.omega.2014.10.004
  • 6. Junaid M., Xue Y., Syed M.W., Li J.Z., Ziaullah M., 2020. A neutrosophic AHP and TOPSIS framework for supply chain risk assessment in automotive industry of Pakistan, Sustainability, 12(1), 154. https://doi.org/10.3390/SU12010154
  • 7. Karatop B., Taşkan B., Adar E., Kubat C., 2021. Decision analysis related to the renewable energy investments in Turkey based on a Fuzzy AHP-EDAS-Fuzzy FMEA approach, Computers & Industrial Engineering, 151, 106958. http://doi.org/10.1016/j.cie.2020.106958
  • 8. Lee H.C., Chang C. T., 2018. Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan, Renewable and Sustainable Energy Reviews, 92, 883-896. http://doi.org/10.1016/j.rser.2018.05.007
  • 9. Liu B., Hu Y., Deng Y., 2018. New failure mode and effects analysis based on D numbers downscaling method, International Journal of Computers, Communications & Control, 13(2), 205-220. http://doi.org/10.15837/ijccc.2018.2.2990
  • 10. Mete S. 2019. Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment, Human and Ecological Risk Assessment: An International Journal, 25(7), 1645-1660. http://doi.org/10.1080/10807039.2018.1546115
  • 11. Mohamed E.A., Youssef, M.M., 2017. Analysis of risk factors and events linked to the supply chain: case of automotive sector in Morocco, Journal of Logistics Management, 6(2), 41-51. https://doi.org/10.5923/j.logistics.20170602.02
  • 12. Moktadir M.A., Ali S.M., Mangla S.K., Sharmy T.A., Luthra S., Mishra N., Garza-Reyes J.A., 2018. Decision modeling of risks in pharmaceutical supply chains, Industrial Management & Data Systems, 118(7), 1388-1412.https://doi.org/10.1108/IMDS-10-2017-0465
  • 13. Nabizadeh M., Khalilzadeh M., 2021. Developing a fuzzy goal programming model for health safety and environment risks based on hybrid fuzzy FMEA-VIKOR method, Journal of Engineering, Design and Technology, 19(2), 317-338. http://doi.org/10.1108/JEDT-09-2019-0245
  • 14. Nakandala D., Lau H., Zhao L., 2017. Development of a hybrid fresh food supply chain risk assessment model, International Journal of Production Research, 55(15), 1-16. https://doi.org/10.1080/00207543.2016.1267413
  • 15. Panchal D., Mangla S.K., Tyagi M., Ram M., 2018. Risk analysis for clean and sustainable production in a urea fertilizer industry, International Journal of Quality and Reliability Management, 35(7), 1459-1476. https://doi.org/10.1108/IJQRM-03-2017-0038
  • 16. Pourmadadkar M., Beheshtinia M.A., Ghods K., 2020. An integrated approach for healthcare services risk assessment and quality enhancement, International Journal of Quality & Reliability Management, 37(9/10), 1183-1208. http://doi.org/10.1108/IJQRM-11-2018-0314
  • 17. Rathore R., Thakkar J.J., Jha J.K., 2021. Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR, International Journal of Quality & Reliability Management, 38(2), 551-580. http://doi.org/10.1108/IJQRM-02-2019-0070
  • 18. Sagnak M., Kazancoglu Y., Ozen Y.D.O., Garza-Reyes J.A., 2020. Decision-making for risk evaluation: integration of prospect theory with failure modes and effects analysis (FMEA), International Journal of Quality & Reliability Management, 37(6/7), 939-956. http://doi.org/10.1108/IJQRM-01-2020-0013
  • 19. Scheu M.N., Tremps L., Smolka U., Kolios A., Brennan F., 2019. A systematic failure mode effects and criticality analysis for offshore wind turbine systems towards integrated condition based maintenance strategies, Ocean Engineering, 176, 118-133. https://doi.org/10.1016/j.oceaneng.2019.02.048
  • 20. Shaker F., Shahin A., Jahanyan S., 2019. Developing a two-phase QFD for improving FMEA: an integrative approach, International Journal of Quality and Reliability Management, 36(8), 1454-1474.https://doi.org/10.1108/IJQRM-07-2018-0195
  • 21. Shan H., Li, Y., Shi J., 2020. Influence of Supply Chain Collaborative Innovation on Sustainable Development of Supply Chain: A Study on Chinese Enterprises, Sustainability. 12 https://doi.org/10.3390/su12072978
  • 22. Shannon C.E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communication Review, 5(1), 3-55.https://doi.org/10.1145/584091.584093
  • 23. Shemshadi A., Shirazi H., Toreihi M., Tarokh M.J., 2011. A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting, Expert Systems with Application, 38, 12160-12167
  • 24. Shen L., Li F., Li C., Wang Y., Qian X., Feng T., Wang C., 2020. Inventory Optimization of Fresh Agricultural Products Supply Chain Based on Agricultural Superdocking, Journal of Advanced Transportation, 1–13. https://doi.org/10.1155/2020/2724164
  • 25. Wang W., Lie X., Chen X., Qin Y., 2019. Risk assessment based on hybrid FMEA framework by consider maker’s psychological behavior character, Computers & Industrial Engineering, 136, 516-527. https://doi.org/10.1016/j.cie.2019.07.051
  • 26. Wu Y., Jia W., Li L., Song Z., Xu C., Liu F., 2019. Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation, Energy, 182, 397-411. https://doi.org/10.1016/j.energy.2019.06.007
  • 27. Yan H., Gao C., elzarka H., Mostafa K., Tang W., 2019. Risk assessment for construction of urban rail transit projects, Safety Science, 118, 583-594. http://doi.org/10.1016/j.ssci.2019.05.042
  • 28. Yazdi M., 2019. Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach, International Journal on Interactive Design and Manufacturing, 13(2), 441–458.https://doi.org/10.1007/s12008-018-0496-2
  • 29. Yazdi M., Nedjati A., Zarei E., Abbassi R., 2020. A reliable risk analysis approach using an extension of best-worst method based on democratic-autocratic decision-making style, Journal of Cleaner Production, 256, 120418. https://doi.org/10.1016/j.jclepro.2020.120418
  • 30. Yener Y., Can G.F., 2021. A FMEA based novel intuitionistic fuzzy approach proposal: Intuitionistic fuzzy advance MCDM and mathematical modeling integration, Expert Systems With Applications, 183, 115413. http://doi.org/10.1016/j.eswa.2021.115413
  • 31. Zadeh L., 1965. Fuzzy Sets. Information and Control. https://doi.org/10.1016/S0019-9958(65)90241-X
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-feef8034-8cec-4b0a-af48-421482ba3cc2
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