Optimizing Supply Chain Risk Management: An Integrated Framework Leveraging Large Language Models

被引:0
|
作者
Zhao, Ming [1 ]
Hussain, Omar [1 ]
Zhang, Yu [1 ]
Saberi, Morteza [2 ]
机构
[1] UNSW Canberra, Sch Business, Canberra, ACT, Australia
[2] Univ Technol Sydney, Sch Comp Sci, Sydney, NSW, Australia
来源
2024 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI 2024 | 2024年
关键词
Supply Chain Risk Management; Large Language Models; Artificial Intelligence in Supply Chain; Risk Identification; Automated Risk Assessment; Predictive Analytics in Supply Chain;
D O I
10.1109/CAI59869.2024.00192
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The integration of Large Language Models (LLMs) in Supply Chain Risk Management (SCRM) is a novel approach to addressing the dynamic and complex challenges of risk identification and categorization in supply chains. This paper introduces a framework that leverages the capabilities of LLMs in automating the risk identification process from news and supplier databases. It also integrates a risk labeling process using the Cambridge Taxonomy of Business Risks (CTBR). A case study involving Apple Inc. as the focal company illustrates the practical application of this framework. Our methodology demonstrates significant efficiency in identifying and categorizing supply chain risks, offering a promising tool for supply chain professionals to enhance resilience and responsiveness in a rapidly evolving risk landscape.
引用
收藏
页码:1057 / 1062
页数:6
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