LLMs for Supply Chain Management
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Abstract (arXiv)
The development of large language models (LLMs) has provided new tools for research in supply chain management (SCM). In this paper, we introduce a retrieval-augmented generation (RAG) framework that dynamically integrates external knowledge into the inference process, and develop a domain-specialized SCM LLM, which demonstrates expert-level competence by passing standardized SCM examinations and beer game tests. We further employ the use of LLMs to conduct horizontal and vertical supply chain games, in order to analyze competition and cooperation within supply chains. Our experiments show that RAG significantly improves performance on SCM tasks. Moreover, game-theoretic analysis reveals that the LLM can reproduce insights from the classical SCM literature, while also uncovering novel behaviors and offering fresh perspectives on phenomena such as the bullwhip effect. This paper opens the door for exploring cooperation and competition for complex supply chain network through the lens of LLMs.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 50 references that are in the map. First posted on arXiv 24 May 2025.
Builds on
37 of 50 references.
References · 37
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- 2023A Survey of Large Language Modelscited by 2,576
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- 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
25 more
- 2021Internet-Augmented Dialogue Generationcited by 217
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Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2026When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets22 Jul 2026
- 2026Forecasting Supply Chain Disruptions with Foresight Learning1 Apr 2026
- 2025Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics22 Dec 2025
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