Author(s)

Jianan Wen

Corresponding Author

Jianan Wen

Abstract

With the deepening of globalization and the continuous extension of supply chain networks, the complexity and vulnerability of supply chains are also increasing. Various risks such as supply interruptions, demand fluctuations, natural disasters, and geopolitical conflicts pose a huge threat to the stability of supply chains. Traditional risk management methods have limited adaptability to complex environments, making it difficult to comprehensively respond to these dynamic changes. In recent years, as an important breakthrough in the field of artificial intelligence, big models have demonstrated unique advantages in supply chain risk management with their powerful data processing, natural language understanding, and intelligent decision-making capabilities. Large models can provide enterprises with more accurate risk identification, comprehensive risk assessment, and efficient emergency response support through multi-source data integration, scenario simulation, and dynamic optimization. This article systematically analyzes the core technologies and application scenarios of large models in supply chain risk management, explores their practical performance in improving risk management efficiency, and proposes path suggestions for technology integration, data governance, and intelligent platform construction. Through theoretical analysis and case studies, this article provides important theoretical basis and practical guidance for enterprises to build a more intelligent supply chain risk management system, helping them enhance their competitiveness and risk resistance in the complex global supply chain environment.

Keywords

Large-scale Models; Supply Chain Risk Management; Artificial Intelligence; Risk Identification and Assessment; Intelligent Decision-making; Supply Chain Resilience