ABSTRACT
This study presents the development of a Decision Support System (DSS) designed to enhance inventory management in a pharmaceutical retail outlet. The system integrates key parameters such as demand rate, lead time, and stock-level data to accurately predict the reorder point for each medicine. Once predictions are generated, the DSS compares actual stock levels with forecasted requirements and sends advisory feedback to the managerial team on the state of inventory. This ensures timely reordering of essential drugs, minimizing the risk of stockouts while preventing excess inventory. By supporting data-driven decision-making, the DSS enables the optimization of inventory levels and ordering processes, leading to improved customer satisfaction, reduced operational costs, and more reliable access to critical medications. In addition, the application incorporates a user-friendly interface and graphical features that simplify data visualization and interpretation, making it easier for managers to analyze inventory trends and respond effectively to changing demand patterns.
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