Optimizing Inventory Management with AI: Leveraging Deep Reinforcement Learning and Neural Networks for Enhanced Demand Forecasting and Stock Replenishment

Abstract

This research paper explores the integration of artificial intelligence, specifically deep reinforcement learning (DRL) and neural networks, into inventory management systems to enhance demand forecasting and stock replenishment processes. The study begins by identifying the limitations of traditional inventory management techniques, which often rely on static and deterministic models that fail to adapt to the dynamic nature of modern supply chains. It then delves into the potential of DRL, a form of machine learning that learns optimal policies through trial and error, in dynamically adjusting inventory policies in response to fluctuating market conditions. The paper proposes a hybrid approach combining DRL with neural networks to process vast amounts of historical and real-time data, thereby improving demand forecasting accuracy. By simulating various inventory scenarios, the study demonstrates that this AI-driven model can significantly reduce stockouts and overstock situations while maximizing service levels and minimizing holding costs. The results indicate that the integration of deep learning techniques into inventory management not only enhances decision-making processes but also leads to cost efficiencies and improved customer satisfaction. This research provides a framework for companies looking to adopt AI technologies in supply chain management, highlighting the practical implications, challenges, and future research opportunities in deploying advanced AI models for inventory optimization.