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Prioritizing Vulnerability Factors of Global Food Supply Chains by Fermatean Fuzzy Analytical Hierarchy Process.

Authors :
Yalcin, Selin
Ayyildiz, Ertugrul
Source :
Foundations of Computing & Decision Sciences; Sep2024, Vol. 49 Issue 3, p303-320, 18p
Publication Year :
2024

Abstract

In response to heightened competition arising from globalization, companies are crafting strategies to sustain their operations. However, these strategies also introduce risks that require meticulous management. The onset of the COVID-19 pandemic has exacerbated disruptions in supply chains, including the vulnerable food supply chain (FCS), strained further by escalating food prices and resource depletion in recent times. Within this context, the vulnerability of global FSCs has escalated significantly due to government-imposed lockdowns during the pandemic. This study aims to comprehensively investigate the multifaceted disruptions in global FSCs caused by the COVID-19 pandemic. By delving deep into the complexities of these disruptions, it seeks to uncover the key factors contributing to the vulnerability of supply chains. Employing a blend of literature review and expert opinions, the study identifies and prioritizes factors using the Fermatean Fuzzy Analytical Hierarchy Process (FF-AHP). A two-level criteria framework consisting of three main criteria and eleven sub-criteria has been developed, taking into account expert recommendations and previous studies. According to the results obtained, it has been revealed that the Managerial factors within the main criteria are the most significant factors in the fragility of the FSC. Among these factors, it has been observed that Technology, Corporation, and Inventory Management are the leading criteria causing to the vulnerability of the FSC. This is the first study to investigate the vulnerabilities of FSC using fuzzy logic. The research underscores the imperative of comprehensive risk management strategies that encompass all stakeholders within the supply chain, particularly during unanticipated crises like pandemics. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08676356
Volume :
49
Issue :
3
Database :
Complementary Index
Journal :
Foundations of Computing & Decision Sciences
Publication Type :
Academic Journal
Accession number :
179762906
Full Text :
https://doi.org/10.2478/fcds-2024-0016