Robustness of automotive supply chain networks based on complex network analysis

Donghui Yang (), Meng Tang and Yongbo Ni
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Donghui Yang: Southeast University
Meng Tang: Southeast University
Yongbo Ni: Southeast University

Electronic Commerce Research, 2025, vol. 25, issue 5, No 8, 3609-3636

Abstract: Abstract Recently, global environmental and political changes have heightened the risk of supply chain disruptions. To bolster the risk resilience of the supply chain, this paper delves into its robustness structure using complex network theory. Firstly, a network of automotive suppliers is established to analyze its structural characteristics. Notably, the network degree distribution manifests a power-law distribution, highlighting an intricate interplay of mutual cooperation within cohesive communities. Subsequently, the paper performs simulation analyses under scenarios involving both random and deliberate attacks on the automotive supply chain network. The focus is on assessing the network efficiency, the size of the largest connected subgraph, and the variations in three distinct module structures. The results indicate that the automotive supply network exhibits good robustness under random attacks but relatively weak robustness under deliberate attacks. Additionally, a hub-like structure demonstrates significant robustness in the automotive supply chain network, showing optimal risk resistance even in the scenario of deliberate attacks. Therefore, when constructing network connections in the automotive supply chain, it is advisable to focus on establishing hub-like connections with upstream and downstream partners, while also preventing both internal and external risks of deliberate attacks, thereby enhancing the overall robustness of the entire supply chain network. These findings provide insights into risk mitigation in terms of network construction for automotive supply chain management and optimization.

Keywords: Automotive supply chain; Complex networks; Robustness assessment; Modularity analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10660-024-09814-9

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