A novel resilience measurement model for supply chains based on risk propagation

With the accelerated pace of globalization, supply chains are being increasingly exposed to complex and multifaceted risks. The accurate measurement and effective enhancement of supply chain resilience (SCR) have therefore emerged as critical research imperatives. Although a number of existing studies have begun to explore the measurement of supply chain resilience from a dynamic perspective, the majority of studies fail to fully consider the cascading effects of risk propagation and the effect of recovery strategies throughout the dynamic process. Therefore, this study proposes a novel SCR measurement model on the basis of risk propagation from complex network theory. By combining dynamic simulation analysis methods, the model simulates the evolutionary processes of supply chains under various risk scenarios. This research reveals that SCR is not solely determined by network structure but is also constrained by node importance and recovery prioritization. A priority recovery strategy based on node importance has significant advantages in terms of enhancing network resilience and recovery speed. Under certain conditions, localized optimization strategies may weaken overall network resilience, thus producing counterproductive effects. This study provides a fresh lens for evaluating SCR by emphasizing the significance of risk propagation and recovery mechanisms in strengthening supply chain robustness. It not only increases the theoretical understanding of resilience assessment but also establishes a scientific basis for improving supply chain configurations and designing practical recovery strategies.