Abstract:

To decouple the structural complexity of industrial and supply chains, address systemic risks, and enhance supply chain resilience, bidding transaction big data was employed. Taking the vaccine sector as an example, a framework for constructing a supply chain knowledge graph was designed, and a systematic supply chain knowledge graph was established. On this basis, complex network techniques were applied to examine the vulnerability and potential security risks of China's vaccine industry supply chain network from 2011 to 2023. The research encompassed complex knowledge queries of the industrial chain, an analysis of city degree distribution patterns, and simulations and analyses of supply chain risks. The study shows that the vaccine industry chain exhibits spatial imbalance, particularly between eastern and western regions. The production structure is highly dependent, with approximately 61.3% of vaccine varieties relying on a single manufacturer or overseas agent. Manufacturers with high centrality constitute potential risk points within the vaccine supply chain network, where disruptions to about 33 enterprises significantly hinder vaccine supply. Compared with core cities, the cumulative effects of cities with lower network status, such as Chongqing, Dalian, Shenzhen, and Shenyang, have a more pronounced impact on the efficiency of vaccine circulation.

Key words: tender transaction big data, vaccine supply chain, knowledge graph, complex network, security risks