Technical gazette, Vol. 32 No. 3, 2025.
Original scientific paper
https://doi.org/10.17559/TV-20240227001351
A Multi-Dimensional Fusion Method for Identifying Key Nodes in New Energy Vehicle Supply Chains
Haiwei Gao
orcid.org/0009-0007-6001-8340
; School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Xiaomin Zhu
; School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Xiaobo Yang
; 1. School of Food and Health, Beijing Technology and Business University, 2. National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
*
Tianyue Liu
; 1. LMIB and NLSDE, Beihang University, 2. Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Institute of Artificial Intelligence, Beihang University, 3. Zhongguancun Laboratory, 4. Peng Cheng Laboratory, Beijing 100191, China
Binghui Guo
; 1. LMIB and NLSDE, Beihang University, 2. Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Institute of Artificial Intelligence, Beihang University, 3. Zhongguancun Laboratory, 4. Peng Cheng Laboratory, Beijing 100191, China
Mingzhe Xu
; School of Economics and Management, Beijing Jiaotong University, Beijing 100044, P. R. China
* Corresponding author.
Abstract
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.
Keywords
automobile supply chain; cascading failure model; complex network; new energy vehicle; SIR model
Hrčak ID:
330585
URI
Publication date:
1.5.2025.
Visits: 397 *