Optimizing inventory management using a multi-agent LLM system

Effective inventory management requires a comprehensive capability to forecast demand and optimize stock levels, traditionally reserved for human expertise. Emerging AI methods, while providing effective solutions through deep learning models and data analytics, often lack the flexibility to incorporate dynamic market insights and real-time data. By leveraging the diverse capabilities of multiple dynamically interacting large language models (LLMs), we can overcome these limitations and develop a new class of AI-driven inventory management systems. This paper presents a multi-agent framework comprising a project manager agent, a sales forecasting agent, and an inventory manager agent, which autonomously collaborate to address inventory management challenges. The agents dynamically adjust inventory plans and maintain product availability through self and mutual corrections. Simulation results demonstrate a significant increase in the inventory turnover ratio, reduced shipping costs and holding fees, and a substantial decrease in total cost, all while maintaining a zero stockout rate. Our framework showcases the potential of synergizing the intelligence of LLMs, the precision of statistical modeling, and the dynamic collaborations among diverse agents, opening novel avenues for automating and optimizing supply chain management.