Modeling Supply Chain Interaction and Disruption: Insights from Real-world Data and Complex Adaptive System
Jiawei Feng, Mengsi Cai, Fangze Dai, Tianci Bu, Xiaoyu Zhang, Huijun, Zheng, Xin Lu

TL;DR
This paper models the complex supply chain interactions of automotive SoC enterprises in China, using knowledge graphs and disruption models to analyze resilience and adaptive behaviors amid various risks.
Contribution
It introduces a novel approach combining knowledge extraction, fuzzy matching, and network analysis to understand supply chain dynamics and disruptions in the automotive SoC industry.
Findings
Supply chain network characteristics and firm centrality correlations identified.
Two disruption models developed to simulate adaptive behaviors under risks.
Insights into resilience strategies for automotive SoC supply chains.
Abstract
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…
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Taxonomy
TopicsSupply Chain Resilience and Risk Management · Big Data and Business Intelligence
