AI-Assisted Pharmaceutical Inventory Optimization

Abstract

Efficient pharmaceutical inventory management is fundamental to ensuring uninterrupted healthcare services while minimizing medicine wastage and operational costs. Traditional inventory control systems often rely on static reorder policies that cannot adequately respond to dynamic demand patterns, fluctuating supplier lead times, and medicine expiration. This research proposes an AI-assisted pharmaceutical inventory optimization framework integrating deep learning-based demand forecasting, expiry-risk prediction, and reinforcement learning-driven inventory decision support. The proposed framework combines Temporal Fusion Transformer models for forecasting medicine demand with Gradient Boosting algorithms for estimating expiry probability and a Deep Q-Network for adaptive inventory optimization. Unlike conventional forecasting-only approaches, the proposed framework simultaneously considers inventory availability, storage cost, expiration losses, and service-level objectives. The study establishes a scalable architecture suitable for hospital pharmacies and regional healthcare distribution centers. The proposed methodology aims to reduce inventory inefficiencies while improving medicine accessibility and operational sustainability in modern pharmaceutical supply chains