Assessing resiliency in scale-free supply chain networks: a stress testing approach based on entropy measurements and value-at-risk analysis

Abstract

Supply chain (SC) resiliency and risk management have garnered increasing attention recently. While several studies have explored the use of scale-free network models to design and optimise SC networks, there remains a lack of a generalised stress-testing method that can be applied to various types and sizes of SCs. To address this, we propose a novel approach to assess the resiliency of scale-free SC networks based on entropy measurements. By considering various sources of disruptions, including demand fluctuations, capacity constraints, and transportation disruptions, we create a stress test that measures the network's resiliency. Our method categorises the SC into plants, ports, and customers and analyses the impact of disruptions on individual nodes within similar categories. We utilise real-world SC logistics data and map it into a scale-free network representation. By calculating the global vulnerability of the entire SC network and the localised impact of node vulnerability, we gain insights into how disruptions propagate and affect interconnected nodes. We use value-at-risk (VaR) and conditional-VaR (CVaR) to identify high-risk nodes, highlighting nodes at risk of extreme performance fluctuations or failures. The analytical results have led to developing six propositions, which serve as references for practitioners and help generalise the findings.