Designing supplier selection strategies under COVID-19 constraints for industrial environments

Silvia Carpitella, Silvia Carpitella, Mzougui Ilyas, Elfelsoufi Zoubir, Mzougui Ilyas

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProcedia CIRP
Number of pages6
Publication statusPublished - 2021

Publication series


All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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