Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure

Abstract

The present study assesses supply chain resilience based on network structural parameters. Resilience is computed as a composite effect of density, centrality, connectivity, and network size of the network. A simulation-based approach is adopted, wherein networks of 23 firms operating in India are subjected to risk combinations of five mutually inclusive independent scenarios of probability levels and five mutually exclusive and exhaustive impact levels. The worst-case performance of the supply chain network when subjected to high impact and low probability risks is captured using conditional-value at risk (CVaR). Results reveal that the firm which has the lowest density and centrality and the highest connectivity and network size, exhibits the highest resilience. Whereas, the firm which has the highest density and high centrality due to an aggregation node exhibits the lowest resilience. The two main contributions of the present study are as follows. First, it derives insights for practicing man-agers from actual instead of theoretical networks. Second, it captures the worst-case performance of the supply chain network using CVaR, which has not been reported by any study in the supply chain network structure domain. The simulation-based approach can be easily adopted by the managers to assess the resilience of their supply chain networks and their preparedness to face potential risks. The information available in the form of CVaR is an important input to practicing managers to evaluate whether their supply chain network can face severe disruptions or not. The managers can then make informed decisions on how to increase the resilience of their supply chain networks.