Assessing Supply Chain Resilience During the Pandemic Using Network Analysis

Abstract

Disruptions induced by the COVID-19 pandemic have wreaked havoc in supply chain networks. To gain an understanding of the dynamics that had been at play, we construct a real supply chain network (scale-free) based on a seed firm (Apple), its customers, and its first- and second-tier suppliers, yielding a network of a total of 883 firms. We then use visualization to derive insight into various network characteristics and develop an agent-based model to capture the disruption of the network over a period of 400 days from the onset of the pandemic. The disruptions experienced by firms depend on the stringency of measures taken to curb the pandemic in their respective countries and the severity of disruptions experienced by suppliers in a specific region. We specifically find that spatial complexity, degree centrality, betweenness centrality, and closeness centrality have changed significantly throughout our observation period. We thus subsequently theorize on the influence of some of these characteristics on supply chain resilience (SCRes), and through our empirical tests, we find that, at the network level, Average degree and spatial complexity significantly influence SCRes. At the firm-level, we find that powerful firms within the network influence SCRes based on their betweenness centrality and closeness Centrality. Implications for managerial practice and academic research are discussed.