The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data
|
|
Author(s): Santosh Kumar Srivastava (Institute of Management Technology, Ghaziabad, India), Susmi Routray (Institute of Management Technology, Ghaziabad, India), Surajit Bag (Research Center, Léonard de Vinci Pôle Universitaire, France), Shivam Gupta (Department of Information Systems, Supply Chain Management and Decision Support, NEOMA Business School, France)and Justin Zuopeng Zhang (University of North Florida, USA)
Copyright: 2024
Volume: 32
Issue: 1
Pages: 29
Source title:
Journal of Global Information Management (JGIM)
Editor(s)-in-Chief: Zuopeng (Justin) Zhang (University of North Florida, USA)
DOI: 10.4018/JGIM.335125
Purchase
|
Abstract
This study aims to identify emerging topics, themes, and potential areas for applying large language models (LLMs) in supply chain management through data triangulation. This study involved the synthesis of 33 published articles and a total of 3421 social media documents, including tweets, posts, expert opinions, and industry reports on utilizing LLMs in supply chain management. By employing BERT models, four core themes were derived: Supply chain optimization, supply chain risk and security management, supply chain knowledge management, and automated contract intelligence, which provides the present status of LLM in the supply chain. The results of this study will empower managers to identify prospective applications and areas for improvement, affording them a comprehensive understanding of the antecedents, decisions, and outcomes detailed in the framework. The insights garnered from this study are highly valuable to both researchers and managers, equipping them to harness the latest advancements in LLM technology and its role within supply chain management.
Related Content
|
Santosh Kumar Srivastava, Susmi Routray, Surajit Bag, Shivam Gupta, Justin Zuopeng Zhang.
© 2024.
29 pages.
|
|
Hsiang-Lan Cheng, Chiew Mei Tan, Chao-Min Chiu, Hsin-Yi Huang, Yi-Chien Lee.
© 2024.
39 pages.
|
|
Zixuan Li, Chengli Wang.
© 2024.
23 pages.
|
|
Lan Zhang, Yuwei Ye, Zixuan Meng, Ning Ma, Chia-Huei Wu.
© 2024.
20 pages.
|
|
Haowei Zhang, Yang Lv, Su Zhang, Yulong David Liu.
© 2024.
20 pages.
|
|
Yongling Zhang, Huaqing Du, Tianyu Piao, Hongyu Shi, Sang-Bing (Jason) Tsai.
© 2024.
26 pages.
|
|
Hyundong Nam, Taewoo Nam, Songeun Kim.
© 2024.
21 pages.
|
|
|