Integrating Demand Forecasting and EOQ for Inventory Management in the Pharmaceutical Sector

Effective inventory management is critical in the pharmaceutical sector, where the availability of essential medications directly impacts patient care. This study investigates the integration of demand forecasting techniques and inventory optimization to improve supply chain efficiency, focusing on GLYMIN, a vital drug for managing type 2 diabetes. Two forecasting methods, Exponential Smoothing and Linear Regression, were evaluated using sales data. The Linear regression has fewer errors with 771.04 MAD, 766,666.29 MSE, and 4.32% MAPE. The study incorporates Linear Regression forecasts into an Economic Order Quantity (EOQ) model to determine optimal inventory parameters, such as safety stock, reorder levels, and average inventory. Sensitivity analysis and Monte Carlo simulations were conducted to assess the impact of lead time and demand variability on inventory costs and stockout probabilities. Stockout probability was found to be 0.0006. The proposed framework offers a scalable approach for other medications and contexts. However, the study is constrained by the size and scope of the dataset, suggesting future work could benefit from larger datasets and hybrid forecasting models to capture seasonality and nonlinear trends.