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The resilience of supply chain networks has become critical concerns in today’s complex business environment. Traditional approaches to supply chain analysis, however, face significant limitations when dealing with complex network structures. This paper addresses the limitations of traditional supply chain analysis in dealing with the complexities of real-world supply chain networks, such as the insufficient consideration of nonlinear effects, inadequate analysis of large-scale networks, and imprecise resilience regulation strategies. To overcome these limitations, we develop a system dynamics model for supply chain networks, drawing upon ecological network dynamical equations. A critical indicator, the environmental effective difficulty, \(\lambda^{*}\) , is characterized, and its functional relationship with key dynamic parameters—self-decay rate \((\beta)\) , collaboration intensity \((\gamma)\) , and weight sensitivity \((h)\) — is derived. The analysis reveals how changes in these parameters impact network resilience. Furthermore, simulations using 2023 CSMAR supply chain data demonstrate the relationship between \(\lambda^{*}\) values and the system’s final steady state. The results provide a theoretical foundation for enhancing supply chain resilience and developing effective management strategies, offering new directions for supply chain research.

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Exploration of Resilience in Nonlinear Collaborative Supply Chain Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Siqing Pang,
  • Xinyue Sun,
  • Yihua Zhou,
  • Liangli Yang,
  • Yutai Zhang,
  • Yixiu Kong

摘要

The resilience of supply chain networks has become critical concerns in today’s complex business environment. Traditional approaches to supply chain analysis, however, face significant limitations when dealing with complex network structures. This paper addresses the limitations of traditional supply chain analysis in dealing with the complexities of real-world supply chain networks, such as the insufficient consideration of nonlinear effects, inadequate analysis of large-scale networks, and imprecise resilience regulation strategies. To overcome these limitations, we develop a system dynamics model for supply chain networks, drawing upon ecological network dynamical equations. A critical indicator, the environmental effective difficulty, \(\lambda^{*}\) , is characterized, and its functional relationship with key dynamic parameters—self-decay rate \((\beta)\) , collaboration intensity \((\gamma)\) , and weight sensitivity \((h)\) — is derived. The analysis reveals how changes in these parameters impact network resilience. Furthermore, simulations using 2023 CSMAR supply chain data demonstrate the relationship between \(\lambda^{*}\) values and the system’s final steady state. The results provide a theoretical foundation for enhancing supply chain resilience and developing effective management strategies, offering new directions for supply chain research.