A Data-Enabled Inventory Rationalization Model for Reducing Stockouts in Public Sector Pharmaceutical Operations
Keywords:
Data-enabled, Inventory, Rationalization model, Stockouts, Public sector, Pharmaceutical operationsAbstract
Stockouts of essential medicines in public sector pharmaceutical operations remain a critical challenge in many low- and middle-income countries, undermining healthcare delivery and patient outcomes. These stockouts often stem from fragmented supply chain systems, inadequate forecasting, inefficient inventory management, and delayed procurement processes. Addressing this issue requires a shift from reactive replenishment to proactive, data-driven inventory planning. This proposes a data-enabled inventory rationalization model specifically designed to reduce stockouts in public sector pharmaceutical supply chains. The model integrates consumption-based forecasting, ABC-VED (Always, Better, Control – Vital, Essential, Desirable) inventory classification, and machine learning-driven analytics to guide procurement and distribution decisions. It leverages historical stock data, lead times, morbidity patterns, and real-time logistics management information system (LMIS) inputs to generate actionable insights. Key components of the model include a demand prediction engine, a stock prioritization matrix, and a decision-support dashboard tailored for health supply planners. By rationalizing inventory according to demand criticality and availability, the model ensures that high-priority and fast-moving items are adequately stocked while minimizing overstock and wastage of less critical items. It also supports dynamic safety stock adjustments based on facility-level usage trends and lead-time variability. The model is designed to operate within the infrastructural and policy constraints typical of public sector health systems, including donor funding cycles and regulatory procurement frameworks. Pilot implementation is recommended in selected regions to assess effectiveness and adaptability. Key performance indicators such as stockout rates, order fulfillment rates, and inventory turnover will be used to evaluate impact. This model offers a scalable, data-informed approach to strengthening pharmaceutical supply chain resilience, ensuring consistent access to essential medicines, and improving overall health system performance. Future iterations may explore integration with blockchain technologies and federated data systems for greater transparency and inter-agency coordination.
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