BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Sumrit Detcharat (Mahidol University, Nakhon Pathom, Thailand), Srisawad Sirima (Mahidol University, Nakhon Pathom, Thailand)
Title
Fuzzy Failure Mode and Effect Analysis Model for Operational Supply Chain Risks Assessment: an Application in Canned Tuna Manufacturer in Thailand
Source
LogForum, 2022, vol. 18, nr 1, s. 77-96, rys., wykr., tab., bibliogr. 31 poz.
Keyword
Łańcuch dostaw, Ryzyko operacyjne, Logika rozmyta
Supply chain, Operational risk, Fuzzy logic
Note
summ.
Abstract
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. (original abstract)
Full text
Show
Bibliography
Show
  1. Butdee S., Phuangsalee P., 2019. Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP, Procedia Manufacturing, 30, 663670. 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 riskmanagement: 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:ZZdoi.org/10.1016Zj.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., Taçkan 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/10.1080/10807039.2018.15461 15
  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.20170602.0 2
  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), 13881412 .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/10.1080/00207543.2016.12674 13
  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), 11831208. 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), 441458 .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
Cited by
Show
ISSN
1895-2038
Language
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.2022.645
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu