Risk Identification and Prioritization in China's New Energy Vehicle Supply Chain: An Integrated Tanimoto Similarity and Fuzzy-DEMATEL Approach
Recently, the burgeoning new energy vehicle (NEV) industry has led to a complex landscape due to the rapid integration of diverse elements including the automotive supply chain and new energy technologies. This study aims to identify and assess risk factors within China's NEV supply chain. We develop a comprehensive risk prioritization system comprising 28 factors categorized into two dimensions: internal and external risks, as well as abilities and status. Using the Fuzzy-DEMATEL method, risk factors in China's NEV supply chain are evaluated. The results indicate that the NEV appears an evident risk characteristic of upstream and external driven, i.e., upstream risk, environmental risk, and political risk exert a significant impact on other risk factors within the supply chain. Contrarily, midstream and downstream aspects are more susceptible to external influences. Additionally, we develop an oversight risk identification approach via integrating Fuzzy-DEMATEL, Tanimoto similarity, and CRITIC methodologies. This approach emphasizes uncovering easily overlooked risk factors originating from discrepancies in expert decision-making, particularly in emerging industries like NEV. The results emphasize the significance of addressing often-neglected risk factors such as pollution, infrastructure setup, and information sharing, warranting attention from governmental and industry stakeholder.
Authors
- Zongsheng Huang (ORCID: https://orcid.org/0000-0002-8545-0259)
- Yaqi Shi (ORCID: https://orcid.org/0009-0000-6433-8041)
- Decui Liang (ORCID: https://orcid.org/0000-0002-0655-1255)
Institutions
- University of Electronic Science and Technology of China (CN)
- Shanghai Maritime University (CN)
Publication Details
- Journal
- IEEE Transactions on Engineering Management
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tem.2025.3556944
- Citations
- 21
- Primary Topic
- Risk and Safety Analysis
- Type
- article
- Field-Weighted Citation Impact
- 29.37