Modeling Supply Chain Interaction and Disruption: Insights from Real-world Data and Complex Adaptive System
<|reference_start|>Modeling Supply Chain Interaction and Disruption: Insights from Real-world Data and Complex Adaptive System: In the rapidly evolving automotive industry, Systems-on-Chips (SoCs) are playing an increasingly crucial role in enhancing vehicle intelligence, connectivity, and safety features. For enterprises whose business encompasses automotive SoCs, the sustained and stable provision and receipt of SoC relevant goods or services are essential. Considering the imperative for a resilient and adaptable supply network, enterprises are concentrating their efforts on formulating strategies to address risks stemming from supply chain disruptions caused by technological obsolescence, natural disasters, and geopolitical tensions. This study presents an open supply knowledge extraction and complement approach and build a supply chain network of automotive SoC enterprises in China, which incorporates cross-domain named entity recognition under limited information, fuzzy matching of firm entities, and supply relation inferring based on knowledge graph. Subsequently, we exhibit the degree and registered capital distribution across firms, and analyze the correlations between centrality metrics in the supply chain network. Finally, based on recovery capacity and risk transfer, two interaction disruption models (IDMs) are developed to elucidate the adaptive behaviors and effect of network disruptions under various business and attack strategies. This research not only aids in exploring the complexities of Chinese automotive SoC supply chain but also enriches our understanding of the dynamics of firm behavior in this crucial industry sector.<|reference_end|>
@article{feng2024modeling,
    title={Modeling Supply Chain Interaction and Disruption: Insights from
  Real-world Data and Complex Adaptive System},
    author={Jiawei Feng, Mengsi Cai, Fangze Dai, Tianci Bu, Xiaoyu Zhang, Huijun
  Zheng, Xin Lu},
    journal={arXiv preprint arXiv:2405.10818},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10818},
    primaryClass={cs.SI}
}