LLMs for Supply Chain Management

TL;DR

This paper introduces a specialized large language model for supply chain management that leverages retrieval-augmented generation to enhance performance and analyze complex supply chain behaviors through game-theoretic experiments.

Contribution

The paper develops a domain-specific SCM LLM with expert-level skills and demonstrates its ability to analyze supply chain dynamics and phenomena like the bullwhip effect.

Findings

RAG improves SCM task performance

LLM reproduces classical SCM insights

Uncovers novel supply chain behaviors

Abstract

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.