Environmental Risk Management in Automaker Industries Case Study: Pre-Paint Part of Iran Khodro Company (IKCO)
Despite abundant resources, the automotive industry is reported to adversely impact the environment owing to the use of heavy machinery, diverse and governmental management policies for car production per hour, remarkable employed labor force, production cycle timing, etc. For this purpose, many studies involving environmental risk management have been conducted. To this aim, the present study has been carried out in pre-paint part No. 2 of IKCO (preparation process). In this regard, using FUZZY FMEA and VIKOR methods, the identified risks were assessed and reformative measures and solutions were classified, respectively. A total of 15 individuals considered HSE experts of IKCO were selected as a statistical sample size according to the Morgan table. Consequently, the high level risks were identified and appropriate solutions were suggested to reduce the environmental effects, and according to achieved scores, “torch adjustments based on compliance report” with the objective of reducing air pollution was selected as the compromise solution. IKCO should consider torch adjustment based on compliance report actions as its first priority.
2. Wells P, Wang X, Wang L, Liu H, Orsato R. More friends than foes? The impact of automobility-as-a-service on the incumbent automotive industry. Technological Forecasting and Social Change. 2020;154:119975. https://doi.org/10.1016/j.techfore.2020.119975
3. Rivera JL, Reyes-Carrillo T. A Framework for Environmental and Energy Analysis of the Automobile Painting Process. Procedia CIRP. 2014;15:171-5. https://doi.org/10.1016/j.procir.2014.06.022
4. Bowles JB, editor An assessment of RPN prioritization in a failure modes effects and criticality analysis. Annual Reliability and Maintainability Symposium, 2003; 2003: IEEE. https://doi.org/10.1109/RAMS.2003.1182019
5. Rafie M, Namin FS. Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system. International Journal of Mining Science and Technology. 2015;25(4):655-63. https://doi.org/10.1016/j.ijmst.2015.05.021
6. Harris P, Viliani F. Strategic health assessment for large scale industry development activities: an introduction. Environmental Impact Assessment Review. 2018;68:59-65. https://doi.org/10.1016/j.eiar.2017.10.002
7. Pescaroli G, Wicks RT, Giacomello G, Alexander DE. Increasing resilience to cascading events: The M.OR.D.OR. scenario. Safety Science. 2018;110:131-40. https://doi.org/10.1016/j.ssci.2017.12.012
8. Salokolaei DD, Esmaili SM. A Hybrid Approach Based on AHP and FMEA Approaches for Risk Assessment of Refinery Construction Projects. World. 2019;8(4):35-41.
9. Delic M, Eyers DR. The effect of additive manufacturing adoption on supply chain flexibility and performance: An empirical analysis from the automotive industry. International Journal of Production Economics. 2020;228:107689. https://doi.org/10.1016/j.ijpe.2020.107689
10. Tazi N, Châtelet E, Bouzidi Y. Using a hybrid cost-FMEA analysis for wind turbine reliability analysis. Energies. 2017;10(3):276.
11. Chin K-S, Chan A, Yang J-B. Development of a fuzzy FMEA based product design system. The International Journal of Advanced Manufacturing Technology. 2008;36(7-8):633-49. https://doi.org/10.1007/s00170-006-0898-3
12. Zhang Z, Chu X. Risk prioritization in failure mode and effects analysis under uncertainty. Expert Systems with Applications. 2011;38(1):206-14.
13. Kutlu AC, Ekmekçioğlu M. Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications. 2012;39(1):61-7.
14. Braglia M, Bevilacqua M. Fuzzy modelling and analytical hierarchy processing as a means of quantifying risk levels associated with failure modes in production systems. Technology, Law and Insurance. 2000;5(3-4):125-34.
15. Braglia M, Frosolini M, Montanari R. Fuzzy criticality assessment model for failure modes and effects analysis. International Journal of Quality & Reliability Management. 2003.
16. Chang CL, Liu PH, Wei CC. Failure mode and effects analysis using grey theory. Integrated Manufacturing Systems. 2001.
17. Wang Y-M, Chin K-S, Poon GKK, Yang J-B. Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications. 2009;36(2):1195-207.
18. Liu H-C, Liu L, Bian Q-H, Lin Q-L, Dong N, Xu P-C. Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications. 2011;38(4):4403-15.
19. Gargama H, Chaturvedi SK. Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability. 2011;60(1):102-10.
20. Grassi A, Gamberini R, Mora C, Rimini B. A fuzzy multi-attribute model for risk evaluation in workplaces. Safety Science. 2009;47(5):707-16. https://doi.org/10.1016/j.ssci.2008.10.002
21. Zalewski P. Risk assessment of LNG carrier systems failure using fuzzy logic. Zeszyty Naukowe/Akademia Morska w Szczecinie. 2011:77-85.
22. Petrović DV, Tanasijević M, Milić V, Lilić N, Stojadinović S, Svrkota I. Risk assessment model of mining equipment failure based on fuzzy logic. Expert Systems with Applications. 2014;41(18):8157-64.
23. Xu K, Tang LC, Xie M, Ho SL, Zhu M. Fuzzy assessment of FMEA for engine systems. Reliability Engineering & System Safety. 2002;75(1):17-29.
24. Tay KM, Lim CP. Fuzzy FMEA with a guided rules reduction system for prioritization of failures. International Journal of Quality & Reliability Management. 2006.
25. Yang Z, Xu B, Chen F, Hao Q, Zhu X, Jia Y, editors. A new failure mode and effects analysis model of CNC machine tool using fuzzy theory. The 2010 IEEE International Conference on Information and Automation; 2010: IEEE.
26. Chang K-H, Cheng C-H, Chang Y-C. Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft Computing. 2010;14(3):285.
27. Bukowski L, Feliks J, editors. Application of fuzzy sets in evaluation of failure likelihood. 18th International Conference on Systems Engineering (ICSEng'05); 2005: IEEE.
28. Tay KM, Teh CS, Bong D, editors. Development of a Fuzzy-logic-based Occurrence Updating model for Process FMEA. 2008 International Conference on Computer and Communication Engineering; 2008: IEEE.
29. Mandal S, Maiti J. Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach. Expert Systems with Applications. 2014;41(7):3527-37. https://doi.org/10.1016/j.eswa.2013.10.058
30. Tzeng G-H, Teng M-H, Chen J-J, Opricovic S. Multicriteria selection for a restaurant location in Taipei. International Journal of Hospitality Management. 2002;21(2):171-87. https://doi.org/10.1016/S0278-4319(02)00005-1
31. Opricovic S, Tzeng G-H. Extended VIKOR method in comparison with outranking methods. European journal of operational research. 2007;178(2):514-29.
32. Nahook HN, Eftekhari M. A feature selection method based on∩-fuzzy similarity measures using multi objective genetic algorithm. Complement. 2013;11(S12):S13.
33. Shahabi H, Khezri S, Ahmad BB, Hashim M. Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena. 2014;115:55-70. https://doi.org/10.1016/j.catena.2013.11.014
34. Jeste DV, Savla GN, Thompson WK, Vahia IV, Glorioso DK, Martin AvS, et al. Association between older age and more successful aging: critical role of resilience and depression. American Journal of Psychiatry. 2013;170(2):188-96.
|Issue||Vol 13 No 2 (2021)|
|Risk management VIKOR technique FUZZY FMEA Automaker industries|
|Rights and permissions|
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|