LLMs for Supply Chain Management
This work addresses supply chain management problems for researchers and practitioners, offering incremental advancements by applying existing LLM methods to a new domain.
The paper tackled supply chain management (SCM) tasks by developing a retrieval-augmented generation (RAG) framework and a domain-specialized LLM, which achieved expert-level competence by passing standardized SCM examinations and beer game tests, with RAG significantly improving performance on SCM tasks.
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.