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Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data

Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data

Santosh Kumar Srivastava, Susmi Routray, Surajit Bag, Shivam Gupta, Justin Zuopeng Zhang
Copyright: © 2024 |Volume: 32 |Issue: 1 |Pages: 29
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9798369324523|DOI: 10.4018/JGIM.335125
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MLA

Srivastava, Santosh Kumar, et al. "Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data." JGIM vol.32, no.1 2024: pp.1-29. http://doi.org/10.4018/JGIM.335125

APA

Srivastava, S. K., Routray, S., Bag, S., Gupta, S., & Zhang, J. Z. (2024). Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data. Journal of Global Information Management (JGIM), 32(1), 1-29. http://doi.org/10.4018/JGIM.335125

Chicago

Srivastava, Santosh Kumar, et al. "Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data," Journal of Global Information Management (JGIM) 32, no.1: 1-29. http://doi.org/10.4018/JGIM.335125

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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.