ARISOF: Analytics-Based Rural Inventory and Supplies Ordering with AI Trend Analysis for Enhancing Health Services

The efficient management of medical inventory and supplies remains a critical challenge in rural health facilities due to manual processes, limited resources, and the absence of data-driven decision support. This study aimed to design and developed AISOF (Analytics-Based Inventory and Supplies Ordering with Forecasting) to enhance the efficiency, accuracy, and responsiveness of inventory management in rural health services. The system development followed the Input–Process–Output (IPO) model and an Agile methodology, incorporating iterative user feedback from pharmacists, physicians, staff, and administrators. AISOF integrates data analytics to monitor inventory levels, analyze consumption patterns, and generate demand forecasts to support timely and informed procurement decisions. The system was developed using a web-based architecture with modern front-end and back-end technologies to ensure accessibility, reliability, and scalability across devices. The implementation of AISOF addresses limitations of existing manual inventory practices by improving stock visibility, reducing the risk of stockouts and overstocking, and strengthening operational planning in rural healthcare settings. The study demonstrates that analytics-based inventory systems with forecasting capabilities can significantly support evidence-based management and contribute to improved service delivery in rural health facilities.