Sana Merchant
Independent Researcher
Gujarat, India
Abstract
Pharmacy inventory management plays a pivotal role in ensuring drug availability, minimizing wastage, and enhancing patient care. Traditional inventory systems often rely on static reorder levels and historical consumption, which do not adapt well to demand fluctuations or changes in prescription trends. Predictive analytics offers a transformative solution by utilizing historical data, seasonal trends, and prescribing behaviors to forecast future demand more accurately. This paper explores the impact of predictive analytics on pharmacy inventory optimization through an in-depth analysis of existing studies, statistical forecasting techniques, and case applications. The literature reveals how predictive models such as ARIMA, exponential smoothing, and regression-based forecasting help reduce stock-outs, overstocking, and operational inefficiencies. By adopting a data-driven forecasting framework, pharmacies can streamline inventory turnover, improve order accuracy, and enhance patient satisfaction. This manuscript presents a comprehensive methodology for implementing predictive analytics in a pharmacy setting, along with simulation-based results that demonstrate improved performance metrics over conventional systems.
Keywords
Pharmacy Inventory, Predictive Analytics, Stock-Out Prevention, Forecasting Models, Healthcare Supply Chain, Inventory Optimization, Regression Analysis, ARIMA Models
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