Adaptive and risk recovery strategies for enhancing supply chain network resilience: a cascading failure perspective
本研究提出一个扩展的负载-容量模型，整合过载与欠载失效模式，并构建包含四种节点状态的连续统一框架，结合自适应与风险恢复策略，通过模拟不同密度网络和随机/定向干扰，揭示了企业级和网络级的韧性管理原则。
Abstract The increasing complexity of global supply chain networks, driven by economic integration, has heightened their susceptibility to cascading failures triggered by various disruptions. Existing studies on risk propagation and response strategies in supply chain networks often model only single failure modes, neglect firm-specific heterogeneity in defining failure characteristics, and treat recovery strategies as discrete interventions rather than continuous adaptive behaviors across varying operational states. To address these shortcomings, this study proposes an extended load-capacity model that integrates the coexistence and interaction of overload and underload failure modes, defines nonlinear functions to capture firm-heterogeneous failure characteristics, and develops a continuous unified framework comprising four distinct node states that integrates proactive and reactive risk recovery strategies for adaptive and risk states. By employing the Barabási–Albert model to construct supply chain networks of varying density and simulating both random and targeted disruptions, the results demonstrate that: (1) firms require a comprehensive risk management framework incorporating forward-looking risk warning mechanisms and dual-track parallel risk response systems, with effective implementation of adaptive recovery strategies focusing on three key dimensions under the equilibrium principle; (2) at the network level, the implementation of adaptive recovery strategies should follow differentiation principles across two critical dimensions—strategic prioritization of core firms and management of inter-firm capability disparities. These insights advance both theoretical understanding and practical approaches for designing resilient supply chain networks capable of preventing and mitigating cascading disruptions.