Leveraging Multi-Agent Reinforcement Learning for Digital Transformation in Supply Chain Inventory Optimization

Abstract

In today’s volatile supply chain (SC) environment, competition has shifted beyond individual companies to the entire SC ecosystem. Reducing overall SC costs is crucial for success and benefits all participants. One effective approach to achieve this is through digital transformation, enhancing SC coordination via information sharing, and establishing decision policies among entities. However, the risk of unauthorized leakage of sensitive information poses a significant challenge. We aim to propose a Privacy-preserving Multi-agent Reinforcement Learning (PMaRL) method to enhance SC visibility, coordination, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning with additional SC connectivity information to improve training performance. The simulation-based evaluation results illustrate that the PMaRL method surpasses traditional optimization methods in achieving cost performance comparable to full visibility methods, all while preserving privacy. This research addresses the dual objectives of information security and cost reduction in SC inventory management, aligning with the broader trend of digital transformation.