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EXPLORING THE CHALLENGES OF USING LARGE LANGUAGE MODELS IN SUPPLY CHAIN MANAGEMENT: A DECISION-MAKING
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DOI：10.17270/J.LOG.001417.png)
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Abstract
En 中文
Background: There is growing interest in improving supply chain management (SCM) using artificial intelligence and large language models (LLMs). However, the use of LLMs or generative AI presents inherent challenges. Therefore, recognizing these challenges within organizations and understanding how they are interrelated is crucial. Although there is an emerging focus on the use of LLMs in SCM, there remains limited peer-reviewed research exploring the challenges associated with their use in the field. Hence, this study aims to identify the challenges of using LLMs in SCM and to examine the interrelationships among these challenges. Materials and methods: The challenges identified in the literature were validated through the opinions of sixteen experts, including supply professionals, AI specialists, and academics. The study employed Interpretive Structural Modeling (ISM) and Matrice d'Impacts Crois & eacute;s Multiplication Appliqu & eacute;e & agrave; un Classement (MICMAC) analysis to develop a framework consisting of autonomous, driving, linkage, and dependent challenges. Results: The findings show that Multiple data points in SCM network (C6) emerges as the key challenge with the highest driving power, whereas Cost of better optimization (C13) and Managerial suspicion toward adopting real-time decision making based on LLM outputs (C14) are the key dependent challenges. The study also highlights non-technical aspects of these challenges, such as trust and legal considerations, and emphasizes technology adoption from a human and managerial perspective. Conclusions: These findings offer valuable insights into the ranking of challenges as well as their driving and dependent relationships. They can help SCM practitioners and LLM developers address these challenges and facilitate the effective adoption of LLMs in SCM.
Keywords:
LLMs
Generative AI
supply chain
logistics
challenges
ISM
Journal
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Papers:
23
Citations:
402
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