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

Zhao, M; Hussain, O; Zhang, Y; Saberi, M

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

Zhao, M Hussain, O Zhang, Y Saberi, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2024 IEEE Conference on Artificial Intelligence (CAI), 2024, 00, pp. 1057-1062
Issue Date:: 2024-07-30

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Field	Value	Language
dc.contributor.author	Zhao, M
dc.contributor.author	Hussain, O
dc.contributor.author	Zhang, Y
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.date	2024-06-25
dc.date.accessioned	2024-09-05T11:06:13Z
dc.date.available	2024-09-05T11:06:13Z
dc.date.issued	2024-07-30
dc.identifier.citation	2024 IEEE Conference on Artificial Intelligence (CAI), 2024, 00, pp. 1057-1062
dc.identifier.isbn	979-8-3503-5410-2
dc.identifier.uri	http://hdl.handle.net/10453/180716
dc.description.abstract	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
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2024 IEEE Conference on Artificial Intelligence (CAI)
dc.relation.ispartof	2024 IEEE Conference on Artificial Intelligence
dc.relation.isbasedon	10.1109/cai59869.2024.00192
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Optimizing Supply Chain Risk Management: An Integrated Framework Leveraging Large Language Models
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Singapore, Singapore
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-05T11:06:11Z
pubs.finish-date	2024-06-27
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2024-06-25
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

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

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http://hdl.handle.net/10453/180716