  Supply chain management (SCM) involves coordinating the flow of goods,
information, and finances across various entities to deliver products
efficiently. Effective inventory management is crucial in today's volatile,
uncertain, complex, and ambiguous (VUCA) world. Previous research has
demonstrated the superiority of heuristic methods and reinforcement learning
applications in inventory management. However, the application of large
language models (LLMs) as autonomous agents in multi-agent systems for
inventory management remains underexplored. This study introduces a novel
approach using LLMs to manage multi-agent inventory systems. Leveraging their
zero-shot learning capabilities, our model, InvAgent, enhances resilience and
improves efficiency across the supply chain network. Our contributions include
utilizing LLMs for zero-shot learning to enable adaptive and informed
decision-making without prior training, providing significant explainability
and clarity through Chain-of-Thought (CoT), and demonstrating dynamic
adaptability to varying demand scenarios while minimizing costs and avoiding
stockouts. Extensive evaluations across different scenarios highlight the
efficiency of our model in SCM.
