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Risk transmission in complex supply chain network with multi-drivers

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  • Wang, Jiepeng
  • Zhou, Hong
  • Jin, Xiaodan

Abstract

Using epidemic model to study supply chain risk transmission, multi-drivers including the enterprise risk preference, the operational robustness and flexibility, the completeness of market information and especially the network topology are explored. The mechanism and evolution of the complex supply chain network risk transmission are discussed. We find that (1) For a complex supply chain network, when the risk transmission is lower than the threshold, the risk tends to die, and when the risk transmission is greater than the threshold, the risk trends to achieve transmission and diffusion, and tends to a non-zero equilibrium point. (2) Risk transmission threshold is positively correlated with the operational robustness and flexibility, the completeness of market information, and immunity rate; while it is negatively correlated with the enterprise risk preference. (3) Risk transmission scale is negatively correlated with the operational robustness and flexibility, the completeness of market information, and immunity rate; while it is positively correlated with the enterprise risk preference. (4) The heterogeneity of network is greater, the risk transmission threshold is greater, the risk transmission scale is lower. The conclusion has important theoretical and practical significance for supply chain risk management.

Suggested Citation

  • Wang, Jiepeng & Zhou, Hong & Jin, Xiaodan, 2021. "Risk transmission in complex supply chain network with multi-drivers," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s096007792030655x
    DOI: 10.1016/j.chaos.2020.110259
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    2. Ping Yu & Zhiping Wang & Yanan Sun & Peiwen Wang, 2022. "Risk Diffusion and Control under Uncertain Information Based on Hypernetwork," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    3. Mu, Dong & Ren, Huanyu & Wang, Chao & Yue, Xiongping & Du, Jianbang & Ghadimi, Pezhman, 2023. "Structural characteristics and disruption ripple effect in a meso-level electric vehicle Lithium-ion battery supply chain network," Resources Policy, Elsevier, vol. 80(C).
    4. Berger, Niklas & Schulze-Schwering, Stefan & Long, Elisa & Spinler, Stefan, 2023. "Risk management of supply chain disruptions: An epidemic modeling approach," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1036-1051.
    5. Jianhua Chen & Ting Yin, 2023. "Transmission Mechanism of Post-COVID-19 Emergency Supply Chain Based on Complex Network: An Improved SIR Model," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Wang, Jiepeng & Zhou, Hong & Sun, Xinlei & Yuan, Yufei, 2023. "A novel supply chain network evolving model under random and targeted disruptions," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    7. Feng, Jian Rui & Zhao, Meng-ke & Lu, Shou-xiang, 2024. "Accident spread and risk propagation mechanism in complex industrial system network," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    8. Wang, Jiepeng & Zhou, Hong & Zhao, Yujie, 2022. "Behavior evolution of supply chain networks under disruption risk — From aspects of time dynamic and spatial feature," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    9. Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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