A Multi-Dimensional Fusion Method for Identifying Key Nodes in New Energy Vehicle Supply Chains

This paper proposes a multi-dimensional fusion method for identifying key nodes in new energy vehicle supply chains, considering the structural characteristics and risk propagation properties of the network. The method integrates network centrality analysis, the SIR model for risk propagation, and the cascading failure model to comprehensively evaluate the importance of nodes in the supply chain network. The proposed method is applied to the supply chain networks of Tesla and Xpeng brands, constructed from industry data. The results reveal that the key nodes with a strong impact on risk propagation include not only core enterprises such as batteries but also accessory industries with hidden leading positions. The proposed method provides a more comprehensive approach to identifying potential and critical risk control nodes in new energy vehicle supply chains, which has significant practical implications for supply chain risk management.