Enhancing the Efficiency of Pharmaceutical Inventory Management in Public Hospit

Abstract

Background and Aims: Efficient management of pharmaceutical inventory is a significant challenge for hospitals today, especially in situations with limited resources and fluctuating drug demands. Inadequate management may lead to drug shortages or overstocking, which directly impacts the quality of care and hospital operating costs. This research aims 1) to study the prioritization of pharmaceuticals and medical supplies in purchasing to align with limited inventory, and 2) to explore methods to enhance the efficiency of inventory management by utilizing multiple approaches together for better results than using a single method.

Methodology: This is a quantitative study, using pharmaceutical inventory data from a state hospital in 2023, with a total of 12,322 items. The samples were selected purposively. Research tools included data recording forms, ABC-VED Matrix analysis, and inventory management formulas. The research process began with collecting purchase, dispensing, and distribution data, followed by data analysis using descriptive statistics, ABC-VED Matrix analysis, EOQ, and Safety Stock calculations to improve inventory management efficiency. The results present pharmaceutical categorization based on importance, demand forecasting, and an appropriate purchasing model.

Results: The findings indicate that 1) pharmaceuticals and medical supplies can be classified according to value and medical importance using ABC-VED Matrix analysis. Drugs with the highest importance (Vital) were distributed across all value categories, with group C, representing low-value items, having the largest number. This demonstrates that managing high-importance drugs requires considering both value and medical importance to avoid shortages of vital but low-value items. 2) Combining multiple inventory management methods, such as Modified Moving Average (MMA), Reorder Point (ROP), Economic Order Quantity (EOQ), and Safety Stock (SS), improves accuracy in demand forecasting and reduces the risk of stockouts. MMA enhances forecasting accuracy, while proper ROP helps mitigate shortages. EOQ and Safety Stock calculations reduce management costs and increase system flexibility.

Conclusion: An optimal purchasing model should integrate various concepts and tools, including EOQ, ROP, Safety Stock, MMA, and modern technologies like AI, IoT, and Machine Learning to achieve maximum efficiency in inventory management, cost reduction, and prevention of stockouts.