Impact of Machine Learning on Drug Demand Forecasting in Pharmaceutical Supply Chains

The pharmaceutical industry is characterized by its complex supply chains, critical product demand, and life‐saving inventory management requirements. This paper investigates the impact of machine learning (ML) techniques on drug demand forecasting—a crucial component for optimizing inventory levels, reducing wastage, and ensuring timely distribution of medications. By integrating historical sales data, seasonality trends, socio-economic indicators, and health data, ML algorithms provide a dynamic and accurate forecasting model. This study reviews literature up to 2018, introduces a novel methodology incorporating ensemble learning and time-series analysis, and evaluates the model against traditional forecasting methods. The findings indicate that ML-based approaches significantly improve forecasting accuracy, reduce supply chain disruptions, and support better decision-making in pharmaceutical logistics. The implications of this research suggest that the adoption of advanced ML techniques can lead to enhanced operational efficiency and improved patient outcomes.