COVID-19 pandemic: Supply chain risk management by integrating Interpretive Structural Modeling and Bayesian belief network

Abstract

The paper proposes a theoretical framework, based on a literature review, that analyzes the links between COVID-19 impacts and supply chain risk mitigation strategies, investigating the role of digitalization as a potential key resource to improve the effectiveness of supply chain resilience. Then, the paper empirically tests the framework through a hybrid causal mapping technique using the frameworks of Interpretive Structural Modelling and Bayesian Belief Networks methods to support supply chain decision making approaches. The findings of this paper can support managers in developing simple and traciable models for assessing interdependences among supply chain disruption sources and to invest effectively in resilience strategies.