AN INTEGRATIVE MODEL FOR DRUG INVENTORY OPTIMIZATION IN PHARMACIES USING WMA AND EOQ CONTINUOUS
Abstract
Efficient drug inventory control is essential for pharmacies to maintain service quality, prevent stockouts, and reduce financial losses caused by excessive inventory. This study develops an integrative inventory optimization model combining ABC analysis, Weighted Moving Average (WMA) forecasting, and the Economic Order Quantity (EOQ) Continuous Review approach. ABC analysis identifies high-priority drugs requiring strict control, WMA forecasts demand for Category A items, and the EOQ model determines optimal order quantity, safety stock, and reorder point. Results show that the integration of forecasting and continuous review improves accuracy in estimating demand fluctuations and reduces total inventory costs compared with existing ordering practices. The originality of this work lies in formalizing the integration of WMA forecasting into EOQ Continuous Review, specifically for pharmaceutical inventory systems. Study limitations include the use of a single-pharmacy dataset, fixed lead-time assumptions, and reliance on only one forecasting method. This integrated approach provides a novel and more responsive solution for pharmaceutical inventory management, as the use of WMA enhances forecast accuracy by emphasizing recent demand shifts, while the EOQ Continuous Review model ensures optimal ordering decisions in real time. Together, these methods create a more adaptive framework that reduces uncertainty, improves stock availability, and minimizes overall inventory costs.
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References
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