AI-Based Pharmaceutical Supply Chain Optimization Using Prophet, LSTM, And Agentic Workflows

Public hospital medicine supply chains face persistent challenges including stockouts, medicine expiry, delayed procurement, and inefficient manual inventory control. While lean management principles provide structured waste reduction methodologies such as Just-In-Time (JIT) and Kanban systems, they lack predictive intelligence and real-time adaptability required in dynamic healthcare environments. This research proposes an AI-Guided Lean Decision Support System integrating machine learning-based demand forecasting, intelligent inventory monitoring, and lean rule enforcement to enhance operational efficiency. The proposed system architecture combines predictive analytics, real-time stock evaluation, automated reorder calculations, and route optimization to create a responsive and resilient medicines supply chain. The framework aims to reduce wastage, improve availability, and enable data-driven decision-making in public hospitals.