Abstract
The nature of supply chains presents a variety of issues related to uncertainties. Under various uncertainties, risk management plays a crucial role in an effective supply chain decision-making. The uncertainty involved in the risk assessment process can be divided into two types: random uncertainty and epistemic uncertainty. Fuzzy theory has been applied to deal with uncertainties. The purpose of this paper is to analyze the role and contribution of the fuzzy logic in the treatment of epistemic uncertainty into supply chain risk management approaches. A literature review process was performed, followed by analysis and discussions on the examined topic. The results revealed that the integration with multicriteria decision-making and disruptive analysis methods are the most common types adopted, with trend to petri nets and multicriteria decision-making approaches. Supply risks are the most studied type and identification and assessment are the most developed processes in supply chain risk management. Although the publications on the subject has been highlighted, they present some limitations related to the holistic complexity of risks in supply chains, the dynamic nature of the environment and the reliability of the background knowledge in the assessment. In that sense, these remarks reveal interesting future researches lines.
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- ABC:
-
Activity-based costing
- AHP:
-
Analytic hierarchy process
- ANP:
-
Analytic network process
- ARP:
-
Aggregate risk potential
- BN:
-
Bayesian network
- CCDEA:
-
Chance constraint data envelopment analysis
- DEA:
-
Data envelopment analysis
- DEMATEL:
-
Decision-making trial and evaluation laboratory
- FAHP:
-
Fuzzy analytic hierarchy process
- FANP:
-
Fuzzy analytic network process
- FDEA:
-
Fuzzy data envelopment analysis
- F-DEMATEL:
-
Fuzzy DEMATEL
- FDIIM:
-
Fuzzy dynamic inoperability input/output model
- FEAHP:
-
Fuzzy extended AHP
- F-MABAC:
-
Fuzzy multi-attributive border approximation area comparison
- FMEA:
-
Failure mode and effects analysis
- FTA:
-
Fault tree analysis
- FUV:
-
Fuzzy utility value
- GPN:
-
Global production networks
- HOR:
-
House of risk
- IIM:
-
Inoperability input/output model
- OPC:
-
Operational process cycle
- OPF:
-
Organizational performance factors
- PLC:
-
Product life cycle
- PN:
-
Petri nets
- ROP:
-
Risk operational practices
- SCOR:
-
Supply chain operations reference model
- SCV:
-
Supply chain visibility
- SFMOP:
-
Stochastic fuzzy multi-objective programming model
- TBL:
-
Triple bottom line
- TOPSIS:
-
Technique of order preference for similarity with the ideal solution
- WFPN:
-
Weighted fuzzy Petri net
- VIKOR:
-
Multicriteria optimization and compromise solution
References
Salehi Heidari, S., Khanbabaei, M., Sabzehparvar, M.: A model for supply chain risk management in the automotive industry using fuzzy analytic hierarchy process and fuzzy TOPSIS. Benchmarking Int J 25(9), 3831–3857 (2018)
Ho, W., Zheng, T., Yildiz, H., Talluri, S.: Supply chain risk management: a literature review. Int J Prod Res 53, 1–39 (2015)
Aven, T.: Risk assessment and risk management: review of recent advances on their foundation. Eur J Oper Res 253(1), 1–13 (2016)
Islam, M.S., Nepal, M.: A Fuzzy-Bayesian model for risk assessment in power plant projects. Proc Comput Sci 100, 963–970 (2016)
Zadeh, L.A.: Fuzzy sets. Inf Control 8(3), 338–353 (1965)
Aqlan, F., Lam, S.S.: A fuzzy-based integrated framework for supply chain risk assessment. Int J.Prod Econ 161, 54–63 (2015)
Hoi-Lam, M., Wai-Hung, C.W.: A fuzzy-based house of risk assessment method for manufacturers in global supply chains. Ind Manage Data Syst 118(7), 1463–1476 (2018)
Radivojević, G., Gajović, V.: Supply chain risk modeling by AHP and Fuzzy AHP methods. J Risk Res 17(3), 337–352 (2014)
Wu, D., Wu, D.D., Zhang, Y., Olson, D.L.: Supply chain outsourcing risk using an integrated stochastic-fuzzy optimization approach. Inf Sci 235, 242–258 (2013)
Dutta, P.: Uncertainty modelling in risk assessment based on Dempster-Shafer theory of evidence with generalized fuzzy focal elements. Fuzzy Inf Eng 7, 15–30 (2015)
Biswas, T.K., Zaman, K.: A Fuzzy-based risk assessment methodology for construction projects under epistemic uncertainty. Int J Fuzzy Syst 21(4), 1221–1240 (2009)
Aven, T., Ylönen, M.: Safety regulations: implications of the new risk perspectives. Reliab Eng Syst Saf 149, 164–171 (2016)
Flage, R., Aven, T., Baraldi, P., Zio, E.: Concerns, challenges and directions of development for the issue of representing uncertainty in risk assessment. Risk Anal 34(7), 1196–1207 (2014)
Feryal Can, G., Toktas, P.: A novel fuzzy risk matrix based risk assessment approach. Kybernetes 47(9), 1721–1751 (2018)
Ross, T.J.: Fuzzy logic with engineering applications, 3rd edn. Wiley, Chichester (2010)
Wang, X., Chan, H.K., Yee, R.W.Y., Diaz-Rainey, I.: A two-stage Fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. Int J Prod Econ 135, 595–606 (2012)
Barbosa-Póvoa, A.P., Da Silva, C., Carvalho, A.: Opportunities and challenges in sustainable supply chain: an operations research perspective. Eur J Oper Res 268, 399–431 (2018)
Sodhi, M.S., Son, B.G., Tang, C.S.: Researchers’ perspectives on supply chain risk management. Prod Oper Manag 21, 1–13 (2012)
Wu, D.D., Olson, D.: Enterprise risk management: a DEA VaR approach in vendor selection. Int J Prod Res 48, 4919–4932 (2010)
Chaudhuri, A., Mohanty, B.K., Singh, K.N.: Supply chain risk assessment during new product development: a group decision making approach using numeric and linguistic data. Int J Prod Res 51(10), 2790–2804 (2013)
Samvedi, A., Jain, V., Chan, F.T.: Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. Int J Prod Res 51, 2433–2442 (2013)
Viswanadham, N., Samvedi, A.: Supplier selection based on supply chain ecosystem, performance and risk criteria. Int J Prod Res 51, 6484–6498 (2013)
Khemiri, R., Elbedoui-Maktouf, K., Grabot, B., Zouari, B.: A fuzzy multi-criteria decision making approach for managing performance and risk in integrated procurement–production planning. Int J Prod Res 55(18), 5305–5329 (2017)
Kutlu, A.C., Ekmekçiog˘lu, M.: Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39(1), 61–67 (2012)
Azadeh, A., Alem, S.M.: A flexible deterministic, stochastic and fuzzy data envelopment analysis approach for supply chain risk and vendor selection problem: simulation analysis. Expert Syst Appl 37, 7438–7448 (2010)
Hung, S.J.: Activity-based divergent supply chain planning for competitive advantage in the risky global environment: a DEMATEL-ANP fuzzy goal programming approach. Expert Syst Appl 38, 9053–9062 (2011)
Shahiar, A., Sadiq, R., Tesfamariam, S.: Risk analysis for oil and gas pipelines: a sustainability assessment approach using fuzzy based bow-tie analysis. J Loss Prev Process Ind 25(3), 505–523 (2012)
Aqlan, F., Mustafa, E.: Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry. J Loss Prev Process Ind 29(1), 39–48 (2014)
Javidi, M., Abdolhamidzadeh, B., Reniers, G., Rashtchian, D.: A multivariable model for estimation of vapor cloud explosion occurrence possibility based on a Fuzzy logic approach for flammable materials. J Loss Prev Process Ind 33, 140–150 (2015)
Ganguly, K.K., Guin, K.K.: A fuzzy AHP approach for inbound supply risk assessment. Benchmarking Int J 20(1), 129–146 (2013)
Kumar, S., Luthra, S., Jakhar, S.: Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA Insights from an Indian case study. Benchmarking Int J 25(8), 2660–2687 (2018)
Zimmer, K., Fröhling, M., Breun, P., Schultmann, F.: Assessing social risks of global supply chains: a quantitative analytical approach and its application to supplier selection in the German automotive industry. J Clean Prod 149, 96–109 (2017)
Rostamzadeh, R., Ghorabaee, M.K., Govindan, K., Esmaeili, A., Nobar, H.B.K.: Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. J Clean Prod 175, 651–669 (2018)
Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch, B.: Analyzing system safety and risks under uncertainty using a bow-tie diagram: an innovative approach. Process Saf Environ Protect 91(1–2), 1–18 (2013)
Yazdi, M., Kabir, S.: A fuzzy Bayesian network approach for risk analysis in process industries. Process Saf Environ Prot 1(111), 507–519 (2017)
Chan, F.T.S., Kumar, N.: Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega Int J Manage Sci 35, 417–431 (2007)
John, A., Paraskevadakis, D., Bury, A., Yang, Z., Riahi, R., Wang, J.: An integrated fuzzy risk assessment for seaport operations. Saf Sci 68, 180–194 (2014)
Kumar, S., Kumar, P., Kumar, B.: Risk analysis in green supply chain using fuzzy AHP approach: a case study. Resour Conserv Recycl 104, 375–390 (2015)
Guo, Y., Meng, X., Wang, D., Meng, T., Liu, S., He, R.: Comprehensive risk evaluation of long-distance oil and gas transportation pipelines using a fuzzy Petri net model. J Nat Gas Sci Eng 33, 18–29 (2016)
Zhou, J., Reniers, G., Zhang, L.: A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry. Chem Eng Sci 174, 136–145 (2017)
Jiang, B., Li, J., Shen, S.: Supply chain risk assessment and control of port enterprises: Qingdao port as case study. Asian J Shipping Logistics 34(3), 198–208 (2018)
Moeinzadeh, P., Hajfathaliha, A.: A combined fuzzy decision making approach to supply chain risk assessment. World Acad Sci Eng Technol 60, 519–535 (2009)
Tabrizi, B.H., Razmi, J.: Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks. J Manuf Syst 32(2), 295–307 (2013)
Xiao, Z., Chen, W., Li, L.: An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation. Appl Math Model 36(4), 1444–1454 (2012)
Mostafaeipour, A., Qolipour, M., Eslami, H.: Implementing fuzzy rank function model for a new supply chain risk management. J Supercomput 73, 3586–3602 (2017)
Aviso, K., Amalin, D., Promentilla, M.A.B., Santos, J.R., Yu, K.D., Tan, R.R.: Risk assessment of the economic impacts of climate change on the implementation of mandatory biodiesel blending programs: a fuzzy inoperability input-output modeling (IIM) approach. Biomass Bioenerg 83, 436–447 (2015)
Niknejad, A., Petrovic, D.: Analysis of impact of uncertainty in global production networks’ parameters. Comput Ind Eng 111, 228–238 (2017)
Yu, M.C., Goh, M.: A multi-objective approach to supply chain visibility and risk. Eur J Oper Res 233(1), 125–130 (2014)
Yang, G., Liu, Y.: Designing fuzzy supply chain network problem by mean-risk optimization method. J Intell Manuf 26(3), 447–458 (2015)
Braglia, M., Frosolini, M., Montanari, R.: Fuzzy criticality assessment model for failure modes and effects analysis. Int J Qual Reliab Manag 20(4), 503–524 (2003)
Pillay, A., Wang, J.: Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Saf 79(1), 69–85 (2003)
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Díaz-Curbelo, A., Espin Andrade, R.A. & Gento Municio, Á.M. The Role of Fuzzy Logic to Dealing with Epistemic Uncertainty in Supply Chain Risk Assessment: Review Standpoints. Int. J. Fuzzy Syst. 22, 2769–2791 (2020). https://doi.org/10.1007/s40815-020-00846-5
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DOI: https://doi.org/10.1007/s40815-020-00846-5