Risk Assessment by a New FMEA Model based on an Extended AHP Method under a Fuzzy Environment

Document Type : Research Article


1 Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

3 Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

4 Khatam University, Tehran, Iran



Risk assessment has an essential role in managing different risks and their effects. A failure mode and effects analysis (FMEA), as one of the most famous risk assessment tools, has frequently been used in a wide range of industries and organizations. In this study, a new fuzzy analytic hierarchy process (AHP)-based FMEA model is introduced for evaluating the risks of various failure modes more precisely. In this model, fuzzy weighted aggregated risk priority numbers (FWARPNs) are taken into consideration instead of risk priority numbers (RPNs) for the failure modes. Moreover, considering that an economic criterion is added to the three main risk factors, the FWARPNs are calculated by utilizing four risk factors of occurrence (O), severity (S), detection (D), and cost (C). The new criterion (C) denotes the required cost for eliminating the effects of failure occurred. Also, the weights of these four risk factors are computed by an extended fuzzy AHP method. For enhancing the efficiency of the proposed model, a novel fuzzy numbers ranking method is also applied in both suggested fuzzy FMEA and AHP methods. This new ranking method is based on creating different horizontal α-cuts in fuzzy numbers. Finally, to indicate the practicability and effectiveness of the proposed model, Kerman Ghete Gostar Casting Plant is considered as a case study in which the risks of toxic gas release are assessed by the suggested fuzzy FMEA model. The obtained results show that the proposed model is a practicable and advantageous risk assessment method in the real world.


Abbasbandy, S. and Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers and Mathematics with Applications, 57(3), 413-419.
Akbarpour, F., Gitipour, S., Baghdadi, M. and Mehrdadi, N. (2020). Health risk assessment of heavy metals in the contaminated soils of Tehran province, Iran. Environmental Energy and Economic Research, 4(4), 309-320.
Amini, E., Baniasadi, M., Vahidi, H., Nematollahi, H., Khatami, M., Amandadi, M., and Safarpour, H. (2020). Affecting factors of knowledge-based companies using fuzzy AHP model, case study Tehran University Enterprise Park. Journal of the Knowledge Economy, 11(2), 574-592.
Baykasoğlu, A. and Gölcük, İ. (2020). Comprehensive fuzzy FMEA model: a case study of ERP implementation risks. Operational Research, 20(2), 795-826.
Bhattacharjee, P., Dey, V. and Mandal, U. K. (2020). Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model. Safety Science, 132, 104967.
Boral, S., Howard, I., Chaturvedi, S. K., McKee, K. and Naikan, V. N. A. (2020). An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA. Engineering Failure Analysis, 108, 104195.
Borhani, F. and Noorpoor, A. (2017). Cancer risk assessment Benzene, Toluene, Ethylbenzene and Xylene (BTEX) in the production of insulation bituminous. Environmental Energy and Economic Research, 1(3), 311-320.
Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95(3), 307-317.
Chu, T. C. and Tsao, C. T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1-2), 111-117.
Deng, Y., Zhenfu, Z. and Qi, L. (2006). Ranking fuzzy numbers with an area method using radius of gyration. Computers & Mathematics with Applications, 51(6-7), 1127-1136.
Fattahi, R. and 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.
Fattahi, R., Tavakkoli-Moghaddam, R., Khalilzadeh, M., Shahsavari-Pour, N. and Soltani, R. (2020). A novel FMEA model based on fuzzy multiple-criteria decision-making methods for risk assessment. Journal of Enterprise Information Management, 33(5),881-904.
Geramian, A., Shahin, A., Minaei, B. and Antony, J. (2020). Enhanced FMEA: An integrative approach of fuzzy logic-based FMEA and collective process capability analysis. Journal of the Operational Research Society, 71(5), 800-812.
Gogus, O. and Boucher, T. O. (1998). Strong transitivity, rationality and weak monotonicity in fuzzy pairwise comparisons. Fuzzy Sets and Systems, 94(1), 133-144.
Gul, M., Yucesan, M. and Celik, E. (2020). A manufacturing failure mode and effect analysis based on fuzzy and probabilistic risk analysis. Applied Soft Computing, 96, 106689.
Hassan, A., Purnomo, M. R. A. and Anugerah, A. R. (2019). Fuzzy-analytical-hierarchy process in failure mode and effect analysis (FMEA) to identify process failure in the warehouse of a cement industry. Journal of Engineering, Design and Technology.
Huang, G., Xiao, L. and Zhang, G. (2020). Improved failure mode and effect analysis with interval-valued intuitionistic fuzzy rough number theory. Engineering Applications of Artificial Intelligence, 95, 103856.
Karatop, B., Taşkan, B., Adar, E. and 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.
Kumar Dadsena, K., Sarmah, S. P. and Naikan, V. N. A. (2019). Risk evaluation and mitigation of sustainable road freight transport operation: a case of trucking industry. International Journal of Production Research, 57(19), 6223-6245.
Li, Z. and Chen, L. (2019). A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method. Engineering Applications of Artificial Intelligence, 77, 136-147.
Liu, H. C., You, J. X. and Duan, C. Y. (2019). An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment. International Journal of Production Economics, 207, 163-172.
Nejad, A. M. and Mashinchi, M. (2011). Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number. Computers and Mathematics with Applications, 61(2), 431-442.
Nejad, F. L., Zadeh, H. R., Fattahi, R. and Vahidi, H. (2013). Assessment and strategic planning for In-Door and Out-Door sports with the application of SWOT analysis and AHP in fuzzy environment. International Journal of Sport Studies, 3(11), 1281-1291.
Omidvar, B., Azizi, R. and Abdollahi, Y. (2017). Seismic risk assessment of power substations. Environmental Energy and Economic Research, 1(1), 43-60.
Padash, A. (2017). Modeling of environmental impact assessment based on RIAM and TOPSIS for desalination and operating units. Environmental Energy and Economic Research, 1(1), 75-88.
Padash, A. and Ataee, S. (2019). Prioritization of environmental sensitive spots in studies of environmental impact assessment to select the preferred option, based on AHP and GIS compound in the gas pipeline project. Pollution, 5(3), 671-685.
Padash, A. and Ghatari, A. R. (2020). Toward an Innovative Green Strategic Formulation Methodology: Empowerment of corporate social, health, safety and environment. Journal of Cleaner Production, 261, 121075.
Padash, A., Vahidi, H., Fattahi, R. and Nematollahi, H. (2021). Analyzing and Evaluating Industrial Ecology Development Model in Iran Using FAHP-DPSIR. International Journal of Environmental Research, Article in Press, 1-15.
Padash, A., Vahidi, H., Nematollahi, H. and Fattahi, R. (2020). Analyzing and evaluating industrial ecology development model in Iran.
Park, J., Park, C. and Ahn, S. (2018). Assessment of structural risks using the fuzzy weighted Euclidean FMEA and block diagram analysis. The International Journal of Advanced Manufacturing Technology, 99(9-12), 2071-2080.
Qin, J., Xi, Y. and Pedrycz, W. (2020). Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method. Applied Soft Computing, 89, 106134.
Rezaee, M. J., Yousefi, S., Eshkevari, M., Valipour, M. and Saberi, M. (2020). Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA. Stochastic Environmental Research and Risk Assessment, 34(1), 201-218.
Saaty, T.L. (1980). The Analytic Hierarchy Process. McGrew-Hill, New York.
Sadeghi, B., Sodagari, M., Nematollahi, H. and Alikhani, H. (2021). FMEA and AHP Methods in Managing Environmental Risks in Landfills: A Case Study of Kahrizak, Iran. Environmental Energy and Economic Research, 5(2), 1-15.
Saeidi Keshavarz, M., Razavian, F., Namjoufar, S. and Zahed, M. A. (2020). A new conceptual model for quantitative fire risk assessment of oil storage tanks in the Tehran refinery, Iran. Environmental Energy and Economic Research, 4(3), 241-249.
Seiti, H., Hafezalkotob, A. and Fattahi, R. (2018). Extending a pessimistic–optimistic fuzzy information axiom based approach considering acceptable risk: Application in the selection of maintenance strategy. Applied Soft Computing, 67, 895-909.
Selim, H., Yunusoglu, M. G. and Yılmaz Balaman, Ş. (2016). A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: application in an international food company. Quality and Reliability Engineering International, 32(3), 795-804.
Vahidi, H., Ghazban, F., Abdoli, M. A., Kazemi, V. D. and Banaei, S. M. A. (2014). Fuzzy analytical hierarchy process disposal method selection for an industrial state; case study Charmshahr. Arabian Journal for Science and Engineering, 39(2), 725-735.
Van Laarhoven, P. J. and Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3), 229-241.
Wan, C., Yan, X., Zhang, D., Qu, Z. and Yang, Z. (2019). An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks. Transportation Research Part E: Logistics and Transportation Review, 125, 222-240.
Wang, Z. X., Liu, Y. J., Fan, Z. P. and Feng, B. (2009). Ranking L–R fuzzy number based on deviation degree. Information Sciences, 179(13), 2070-2077.
Wang, L., Yan, F., Wang, F. and Li, Z. (2021). FMEA-CM based quantitative risk assessment for process industries—A case study of coal-to-methanol plant in China. Process Safety and Environmental Protection, 149, 299-311.
Yucesan, M. and Gul, M. (2021). Failure modes and effects analysis based on neutrosophic analytic hierarchy process: method and application. Soft Computing, Article in Press, 1-18.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.