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Cascade Failure-Based Identification and Resilience of Critical Nodes in Automotive Supply Chain Networks

Author

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  • Chengyang Ou

    (School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China)

  • Fubin Pan

    (School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China)

  • Shuangjiao Lin

    (School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China)

Abstract

In the case of cascade failure, due to the close connection of the automobile supply chain network, the chain reaction caused by it should not be ignored; therefore, to find out the important nodes in the automobile supply chain network, to reduce the damage of cascade failure on the supply chain network, and to improve the destruction resistance of the automobile supply chain network is a problem that we should focus on. This paper takes Tesla’s new energy automotive supply chain network as an example to study the impact of cascade failure on the destructive resistance of the automotive supply chain network. From the analysis of the identification results, it is found that the key nodes in the automobile supply chain network with strong influence on risk propagation are mostly charging pile enterprises, motor enterprises, and electronic control enterprises at the core, such as Hengdian Electromagnetics, Wanma Stocks, etc. Meanwhile, Changxin Science and Technology, as a central control panel manufacturer with a large number of indirect suppliers, is also in the top position. Through the proposed key node identification method, it has good practical application value for preventing risk transmission in the automotive supply chain.

Suggested Citation

  • Chengyang Ou & Fubin Pan & Shuangjiao Lin, 2024. "Cascade Failure-Based Identification and Resilience of Critical Nodes in Automotive Supply Chain Networks," Sustainability, MDPI, vol. 16(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5514-:d:1424423
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    References listed on IDEAS

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    1. Li, Yuhong & Zobel, Christopher W., 2020. "Exploring supply chain network resilience in the presence of the ripple effect," International Journal of Production Economics, Elsevier, vol. 228(C).
    2. Yang, Guizhen & Qi, Xiaogang & Liu, Lifang, 2020. "Research on network robustness based on different deliberate attack methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
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