COVID-19 has been impacting worldwide supply chains causing interruption, closure of production and distribution. This impact has been drastic on the supplier side and, as a consequence of disruptions, strong reductions of production have been estimated. Such a circumstance forces companies to propose innovative best practices of supply chain risk management aimed at facing vulnerability generated by COVID-19 and pursuing industrial improvements in manufacturing and production environments. As a part of supply chain strategy, supplier selection criteria should be revised to include pandemic-related risks. This article aims to propose an answer to such a problem. In detail, a comprehensive tool designed as a hybrid combination of multi-criteria decision-making (MCDM) methods is suggested to manage important stages connected to the production development cycle and to provide companies with a structured way to rank risks and easily select their suppliers. The main criteria of analysis will be first identified from the existent literature. Risks related to COVID-19 will be then analysed in order to elaborate a comprehensive list of potential risks in the field of interest. The Best Worst Method (BWM) will be first used to calculate criteria weights. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) will be then applied to rank and prioritize risks affecting suppliers. The effectiveness of the approach will be tested through a case study in the sector of automotive industry. The applicability of the designed MCDM framework can be extended also to other industrial sectors of interest.

Department of Decision Making Theory Institute of Information Theory and Automation Czech Academy of Sciences Prague 18208 Czech Republic

Faculty of Science and Technologies Abdelmalek Essaadi University Tangier 90000 Morocco

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Narasimhan R., Talluri S. Perspectives on risk management in supply chains. Journal of Operations Management. 2009;27(2):114–118.

Ivanov D. Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management. 2017;11(1):24–43.

Hald K., Kinra A. How the blockchain enables and constrains supply chain performance. International Journal of Physical Distribution & Logistics Management. 2019;49(4):376–397.

Hosseini S., Ivanov D., Dolgui A. Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E. 2019;125:285–307.

Ivanov D. Predicting the impact of epidemic outbreaks on the global supply chains: a simulation-based analysis on the example of Coronavirus (COVID-19/SARS-CoV-2) case. Transportation Research -Part E. 2020;136:101922. PubMed PMC

Lin Q., Zhao S., Gao D., Lou Y., Yang S., et al. A conceptual model for COVID-19 outbreak in Wuhan, China with individual reaction and governmental action. International Journal of Infectious Diseases. 2020;93:211–216. PubMed PMC

Sherman, E. (2020) 94% of the Fortune 1000 are seeing coronavirus supply chain disruptions: Report. https://fortune.com/2020/02/21/fortune-1000-coronavirus-china-supply-chain-impact/.

Institute for Supply Chain Management-ISM. (2020) Coronavirus impact on supply chain. https://weareism.org/coronavirus-ism.html.

Mamani H., Chick S.E., Simchi-Levi D. A game-theoretic model of international influenza vaccination coordination. Management Science. 2013;59(7):1650–1670.

Büyüktahtakın E., des-Bordes E., Kıbış E.Y. A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa. European Journal of Operational Research. 2018;265(3):1046–1063.

Anparasan A.A., Lejeune M. Analyzing the response to epidemics: Concept of evidence-based Haddon matrix. Journal of Humanitarian Logistics and Supply Chain Management. 2017;7(3):266–283.

Parvin H., Beygi S., Helm J.E., Larson P.S., Van Oyen M.P. Distribution of medication considering information, transhipment, and clustering: Malaria in Malawi. Production and Operations Management. 2018;27(4):774–797.

Sarkis J., Cohen M.J., Dewick P., Schr P. A brave new world: lessons from the COVID-19 pandemic for transitioning to sustainable supply and production. Resources, Conservation and Recycling. 2020;159:104894. PubMed PMC

Sanjoy K.P. Supplier selection for managing supply risks in supply chain: a fuzzy approach. The International Journal of Advanced Manufacturing Technology. 2015;79:657–664.

Kilincci O., Onal S.A. Fuzzy AHP approach for supplier selection in a washing machine company. Expert Systems with Applications. 2011;38:9656–9664.

Pi W.-N., Low C. Supplier evaluation and selection via Taguchi loss functions and an AHP. International Journal of Advanced Manufacturing Technology. 2006;27(5-6):625–630.

Sevkli M., Koh S.C.L., Zaim S., Demirbag M., Tatoglu E. An application of data envelopment analytic hierarchy process for supplier selection: A case study of BEKO in Turkey. International Journal of Production Research. 2007;45(9):1973–2003.

Dai J., Blackhurst J. A four-phase AHP-QFD approach for supplier assessment: a sustainability perspective. International Journal of Production Research. 2012;50:5474–5490.

Haq A.N., Kannan G. Fuzzy analytical hierarchy process for evaluating and selecting a vendor in a supply chain model. International Journal of Advanced Manufacturing Technology. 2006;29:826–835.

Chan F.T.S., Kumar N., Tiwari M.K., Lau H.C.W., Choy K.L. Global supplier selection: a fuzzy-AHP approach. International Journal of Production Research. 2008;46(14):3825–3857.

Chamodrakas I., Batis D., Martakos D. Supplier selection in electronic marketplaces using satisficing and FAHP. Experts Systems with Applications. 2009;37(1):490–498.

Azadnia A.H., Saman M.Z.M., Wong K.Y. Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process. International Journal of Production Research. 2015;53:383–408.

Govindan K., Khodaverdi R., Jafarian A. A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production. 2013;47:345–354.

Su CM., Horng D.J., Tseng M.L., Chiu A.S.F., Wu K.J., Chen H.P. Improving sustainable supply chain management using a novel hierarchical grey-DEMATEL approach. Journal of Cleaner Production. 2016;134:469–481.

Streimikiene D., Balezentis T., Krisciukaitienė I., Balezentis A. Prioritizing sustainable electricity production technologies: MCDM approach. Renewable and Sustainable Energy Reviews. 2012;16(5):3302–3311.

Liu A.J., Xiao Y.X., Ji X.H., Wang K., Tsai S.B., Lu H., Cheng J.S., Lai X.J., Wang J.T. A Novel Two-Stage Integrated Model for Supplier Selection of Green Fresh Product. Sustainability. 2018;10(7):2371.

Kusi-Sarpong S., Gupta H., Sarkis J. A supply chain sustainability innovation framework and evaluation methodology. International Journal of Production Research. 2018;6:1–9.

Kuo T.C., Hsu C.W., Li J.Y. Developing a Green Supplier Selection Model by Using the D ANP with VIKOR. Sustainability. 2015;7:1661–1689.

Rezaei J., Nispeling T., Sarkis J., Tavasszy L. A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production. 2016;135:577–588.

Khaleie S., Fasanghari M., Tavassoli E. Supplier selection using a novel intuitionist fuzzy clustering approach. Applied Soft Computing. 2012;12:1741–1754.

Haghighi P.S., Moradi M., Salahi M. Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering. International Journal of Intelligent Systems and Applications. 2014;5:76–82.

Luthra S., Govindan K., Kannan D., Mangla S.K., Garg C.P. An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production. 2017;140:1686–1698.

Nekooie M.A., Sheikhalishahi M., Hosnavi R. Supplier selection considering strategic and operational risks:A combined qualitative and quantitative approach. Production engineering:research&development. 2015;9:665–673.

Paul S.K. Supplier selection for managing supply risks in supply chain: A fuzzy approach. International Journal of Advanced Manufacturing Technology. 2015;79:657–664.

Rezaei J. Best-worst multi-criteria decisionmaking method. Omega. 2015;53:49–57.

Rezaei J. Best-worst multi-criteria decisionmaking method: Some properties and a linear model. Omega. 2016;64:126–130.

Chen C.T. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems. 2000;114(1):1–9.