用大模型提升供应链管理，能考专家级试题还能玩博弈游戏。
LLMs for Supply Chain Management
- 用检索增强生成动态引入外部知识，提升推理能力
- 在供应链考试和啤酒游戏测试中表现接近专家水平
- 可模拟供应链竞争合作，发现经典效应的新规律
大型语言模型（LLMs）的发展为供应链管理（SCM）研究提供了新工具。本文提出一种检索增强生成（RAG）框架，动态整合外部知识以增强推理能力，并开发了一个领域专用的供应链管理大模型，该模型在标准化供应链考试和啤酒游戏测试中表现出专家级水平。进一步，我们利用大模型开展横向与纵向供应链博弈实验，分析供应链中的竞争与合作关系。实验表明，RAG显著提升了供应链任务的表现；博弈论分析显示，该模型不仅能复现经典供应链文献中的洞察，还揭示了新颖行为模式，对牛鞭效应等现象提供了新视角。本研究为通过大模型探索复杂供应链网络中的协作与竞争打开了新路径。
原文摘要 · Abstract (English)
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.
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