Document Type : Original Article


Yazd University, Yazd, Iran.


For reducing risk effects in a supply chain, the appropriate risk assessment and ranking by the use of multi-criteria decision-making methods (MCDM) is important. Failure to properly assess and rank the risks makes the supply chain less efficient and competitive. Given the existence of both qualitative and quantitative criteria in a supply chain, the use of verbal preferences, given by authorities for determining the priority of qualitative factors, has higher reliability than that of the Crisp numbers. Fuzzy concept plays an important role in solving the problem of complexity of assigning quantitative fixed numbers to the values of verbal preferences. In the proposed method of this study, a comparison was made among the decision-making methods in the fuzzy environment for selecting a suitable method. To validate the proposed method, we compared it to some case studies from the literature. The results show that the proposed method has high validity and reliability in assessing the risks of a supply chain.


Abarqhouei, N.S., Hosseini Nasab, H., and Fakhrzad, MB., (2012). "Design of the evaluation model for total ergonomics interventions with fuzzy approach", Sci. J. Pure Appl. Sci., Vol. 1, pp. 119-129.
Babaee Tirkolaee, E. Mardani, A., Dashtian, Z., Soltani M., and Weber, G-W., (2020). "A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design", Journal of Cleaner Production,  Vol. 250, pp. 119517.
Büyüközkan, G., and Göcer, F., (2017). "Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem", Applied Soft Computing, Vol. 52, pp. 1222–1238.
Büyüközkan, G., Güleryüz, S., and Karpak, B., (2017). "A new combined IF-DEMATEL and IF-ANP approach for CRM partner valuation", International Journal of Production Economics, Vol. 191, pp. 194–206.
Büyüközkan, G., Karabulutb, Y., and Arsenyanc J., (2017). "RFID service provider selection: An integrated fuzzy MCDM approach", Measurement, Vol. 112, pp. 88–98.
Çolaka, M., and Kayab İ., (2017). "Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey", Renewable and Sustainable Energy Reviews, Vol. 80, pp. 840–853.
Dong, Q., and Cooper, O., (2016). "An orders of magnitude AHP supply chain risk assessment framework", International Journal of Production Economics, Vol. 182, pp. 144–156.
Eslamipoor, R., Fakhrzad, MB., and Zare Mehrjerdi, Y., (2015). "A new robust optimization model under uncertainty for new and remanufactured products", International Journal of Management Science and Engineering Management, Vol. 10, No. 2, pp. 137-142. 
Fang, J., Zhao, L., Fransoo, J.C., and Van Woense, T., (2013). "Sourcing strategies in supply risk management: an approximate dynamic programming approach", Computers and Operations Research, Vol. 40, No. 5, pp. 1371–1382.
Hajian-Heidary M., and Aghaie, A., (2015). "Risk measurement in the global supply chain using monte-carlo simulation", Journal of Industrial Engineering and Management Studies, Vol. 2, No. 2, pp. 1-12.
Hejazi, T.H., and Soleimanmeigouni, I., (2014). "A novel approach in robust group decision making for supply strategic planning in manufacturing networks", Journal of Industrial Engineering and Management Studies, Vol. 1, No. 1, pp. 20-30.
Hoffman, H., Busse, C., Bode, C., and Henke, M., (2014). "Sustainability-related supply chain risks: conceptualization and management", Business Strategy and the Environment, Vol. 23, No. 3, pp. 160-172.
Kavilal, E.G.S., Venkatesan, P., and Harsh Kumar, K.D., (2017). "An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer", Resources Policy, Vol. 51, pp. 204–218.
Khalifehzadeh, S., and Fakhrzad, MB., (2019). "A modified firefly algorithm for optimizing a multi stage supply chain network with stochastic demand and fuzzy production capacity", Computers & Industrial Engineering, Vol. 133, pp. 42-56.
Li, C., Ren, J., and Wang, J.H., (2016). "A system dynamics simulation model of chemical supply chain transportation risk management systems", Computers and Chemical Engineering, Vol. 89, pp. 71–83.
Liou, J.J.H., and Lo, H-W., (2018). "A novel multiple-criteria decision-making-based FMEA model for risk assessment", Applied Soft Computing Journal, Vol. 73, pp. 684–696.
Liu, Hu-C., Song, W., and Ming, X., (2017). "Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method", Journal of Cleaner Production, Vol. 143, pp. 100-115.
Mishra, D., Sharma, R.R.K., Kumar, S., and Dubey, R., (2016). "Bridging and buffering -Strategies for mitigating supply risk and improving supply chain performance", International Journal of Production Economics, Vol. 180, pp. 183–197.
Moradian M., Modanloo V., and Aghaiee S., (2018). "Comparative analysis of multi criteria decision making techniques for material selection of brake booster valve body", traffic and transportation engineering (English edition).
Mufazzal, S., and Muzakkir, S.M., (2018). "A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals", Computers & Industrial Engineering, Vol. 119, pp. 427–438.
Rostamzadeh, R., Ghorabaee, M.K., Govindan, K., Esmaeili, A., Khajeh Nobar, H.B., (2018). "Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach", Journal of Cleaner Production, Vol. 175, pp. 651-669.
Sadra Abarqhouei, N., Hosseini Nasab, H., and Fakhrzad, MB., (2012). "Macro ergonomics interventions and their impact on productivity and reduction of musculoskeletal disorders: including a case study", Iran occupational health, Vol. 9, No. 2, pp. 27-39.
Sarkara, S., Pratiharb, D.K., and Sarkarc, B., (2018). "An integrated fuzzy multiple criteria supplier selection approach and its application in a welding company", Journal of Manufacturing Systems, Vol. 46, pp. 163–178.
Senthil, S., Murugananthan, K., and Ramesh A. (2018). "Analysis and prioritization of risks in a reverse logistics network using hybrid multi-criteria decision making methods", Journal of Cleaner Production, Vol. 179, pp. 716-730.
Shams Esfandabadi, Z., and Seyyed Esfahani, M.M., (2018). "Identifying and classifying the factors affecting risk in automobile hull insurance in Iran using fuzzy Delphi method and factor analysis", Journal of Industrial Engineering and Management Studies, Vol. 5, No. 2, pp. 84-96.
Torkabadi, A.M., Pourjavad, E., and Mayorga, R.V., (2018). "An integrated fuzzy MCDM approach to improve sustainable consumption and production trends in supply chain", Sustainable Production and Consumption, Vol. 16, pp. 99–109.
Wagner, S.M., and Neshat, N., (2010). "Assessing the vulnerability of supply chains using graph theory", International Journal of Production Economics, Vol. 126, pp. 121-129.
Xu, Z., Gu, J., Xia, X., and He, Y., (2017). "An approach to evaluating the spontaneous and contagious credit risk for supply chain enterprises based on fuzzy preference relations", Computers & Industrial Engineering, Vol. 106, pp. 361–372.
Zarbakhshnia, N., Soleimani, H., and Ghaderi, H., (2018). "Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria", Applied Soft Computing, Vol. 65, pp. 307–319.
Zimmermann, H.J., (2011). Fuzzy Set Theory—and its Applications, Springer Science & Business Media.