新能源汽车供应链的关键风险节点识别方法被引量：5

Key Risk Node Identification Methods in New Energy Vehicle Supply Chain

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摘要以在新能源汽车行业中具有典型分析价值的特斯拉和小鹏汽车的公开数据为对象,分别构建了供应链网络模型,并针对供应链网络结构关联特征开展多维度融合的风险传播关键节点识别方法研究。首先,引入网络中心性指标,分析计算得到了中心性指标下的关键节点企业。同时,考虑到汽车供应链风险系统性传播的特点,引入风险免疫传播模型对网络中影响系统性风险传播的关键节点进行判定。最后,对两个网络分别进行级联失效模型分析,选出级联失效模型下在失效发生时对网络影响较大的关键节点。通过多维度的关键节点分析,发现新能源汽车供应链网络中对风险传播有很强影响力的关键节点不仅包含传统意义上的电池等核心企业,而且包含了具有行业隐形冠军属性的配件企业。通过提出的结构和传播属性综合分析方法,可以很好地发现新能源汽车供应链网络中潜在的隐性关键风险控制节点,具有很好的实践应用价值。 In this paper,the open data of Tesla and XPENG,which have typical analytical value in the new energy automobile industry,are respectively used to construct networksof supply chain.And the key node identification method of risk transmission based on multi-dimensional fusion is studied according to the structural correlation characteristics of the network of supply chain.Firstly,the network centrality characteristics are introduced to analyze and calculate the key node.At the same time,considering the characteristics of systemic risk transmission in supply chain of automotive,the risk immune transmission model is introduced to determine the key nodes.Finally,the cascading failure model of the two networks is analyzed respectively,and the key nodes that have strong impact on network failure are selected.Through multi-dimensional key node analysis,it is found that the key nodes with strong impact include not only core enterprises such as batteries in the traditional sense,but also accessory enterprises with invisible leading position.Therefore,through the comprehensive analysis method of structure and transmission attribute proposed in this paper,the potential hidden key risk control nodes in the network of supply chain of new energy vehicles can be well found,which has good practical application value.

作者杨小博高海伟刘天越郭炳晖 YANG Xiaobo;GAO Haiwei;LIU Tianyue;and GUO Binghui(Key Laboratory of Mathematics,lnformatics and Behavioral Semantics,School of Mathematical Sciences,Beihang University,Beijing100191,China;Institute of Artificial Intelligence,Beihang University,Beijing 100191,China;State Key Laboratory of Software Development Enviroment,Beijing 100191,China;Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing,Beijing 100191,China;Zhongguancun Laboratory,Beijing 100094,China;Peng Cheng Laboratory,Shenzhen,Guangdong 518055,China;School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing 100044,China)