Abstract

This research explores the development of an Intelligent Collaborative Supply Chain Management (iCSCM) system, driven by Large Language Models (LLMs), to enhance operational efficiency and facilitate collaboration among academic, governmental, and private sectors in advancing University Holding Companies (UHCs). Despite significant progress in AI-driven supply chain management, challenges remain in effectively aligning academic research with industry demands, resulting in suboptimal resource utilization and missed opportunities for commercialization. This study seeks to address these issues by proposing an AI-driven framework to optimize collaboration within this ecosystem. Employing system design frameworks, architectural evaluation matrices, and expert surveys, the study evaluates the proposed system’s effectiveness, demonstrating a high level of suitability (Mean = 4.73, SD = 0.30). The findings underscore the transformative potential of large language models in enhancing collaborative supply chain processes, equipping universities to serve as key innovation hubs that bridge the gap between research and industry applications. © Little Lion Scientific.

Author keywords

Artificial Intelligence; Intelligent Collaborative Supply Chain Management; Large Language Models; University Holding Company

Siriluk Phuengrod, Panita Wannapiroon and Prachyanun Nilsook (2025)
The Intelligent Collaborative Supply Chain Management with Large Language Models. Journal of Theoretical and Applied Information Technology. 15th March 2025 Vol. 103. No. 5, 2025
https://www.jatit.org/volumes/Vol103No5/20Vol103No5.pdf