AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval

TL;DR

This paper explores the use of large language model-based multi-agent systems for inventory management in supply chains, demonstrating their potential to make optimal decisions and adapt to different scenarios with a novel experience-retrieval agent.

Contribution

It introduces AIM-RM, a new agent that improves adaptability by leveraging historical experience similarity matching, advancing LLM-based supply chain decision systems.

Findings

LLM-based MAS can determine optimal ordering in restricted scenarios

AIM-RM outperforms benchmarks across various supply chain scenarios

The proposed approach demonstrates robustness and adaptability

Abstract

This study investigates large language model (LLM) -based multi-agent systems (MASs) as a promising approach to inventory management, which is a key component of supply chain management. Although these systems have gained considerable attention for their potential to address the challenges associated with typical inventory management methods, key uncertainties regarding their effectiveness persist. Specifically, it is unclear whether LLM-based MASs can consistently derive optimal ordering policies and adapt to diverse supply chain scenarios. To address these questions, we examine an LLM-based MAS with a fixed-ordering strategy prompt that encodes the stepwise processes of the problem setting and a safe-stock strategy commonly used in inventory management. Our empirical results demonstrate that, even without detailed prompt adjustments, an LLM-based MAS can determine optimal ordering decisions in a restricted scenario. To enhance adaptability, we propose a novel agent called AIM-RM, which leverages similar historical experiences through similarity matching. Our results show that AIM-RM outperforms benchmark methods across various supply chain scenarios, highlighting its robustness and adaptability.