Balancing risk and resilience: how network structures and firm strategies mitigate supply chain disruptions

Purpose This study examines how network characteristics—specifically centrality and clustering—influence supply chain disruption risk in the context of the U.S. automotive sector. The paper investigates the moderating effects of firm-level risk management strategies—detection, mitigation, and recovery—on the relationship between network structure and disruption risk, addressing a critical gap in understanding the interplay between network position and risk management effectiveness. Design/methodology/approach A network model of automotive product flows was constructed using secondary data, and risk management strategies were stochastically integrated from a scenario-based vignette experiment. An agent-based contagion simulation modeled disruption propagation throughout the network. Generalized least squares regression with random effects was then employed to analyze how network characteristics and risk management strategies influence disruption risk. Findings The findings indicate a curvilinear relationship between network clustering and disruption risk, showing vulnerabilities at both extremely high and low levels of clustering. In contrast, centrality exhibited a predominantly linear relationship with disruption risk. Firm-level risk management strategies moderate these relationships differently, with detection and recovery strategies significantly attenuating the negative impacts of network vulnerabilities, while mitigation showed limited moderating effectiveness. Originality/value This research contributes to supply chain risk management literature by empirically exploring how firm-level strategies interact with network-level constructs to shape disruption risk. It challenges existing assumptions about linear relationships in network theory, providing nuanced insights for practitioners on tailoring risk management based on network position.