A Cloud-Based Intelligent System for Medicine Inventory Management and Automated Expiry Alerting Using Python and AWS Services

Abstract: Effective management of pharmaceutical stock is critical to patient safety and operational efficiency, yet many pharmacies and healthcare facilities still rely on manual registers or isolated spreadsheets that fail to flag medicines approaching expiry or running low. Such practices lead to the dispensing risk of expired drugs, avoidable financial loss from wastage, and frequent stock-outs of essential items. This paper presents a cloud-based intelligent system that automates medicine inventory tracking and proactively issues expiry and low-stock alerts. The platform is built on a serverless cloud foundation in which Python services handle inventory logic and expiry computation, a scheduled function periodically evaluates stock against configurable thresholds, and a lightweight machine-learning component estimates demand to recommend reorder quantities. A Node.js client provides a responsive interface for pharmacists and administrators, while notifications are delivered through a managed messaging service via email and short message. Inventory records are stored in a managed NoSQL database, and object storage retains supporting documents. Experimental evaluation against manual and rule-based baselines shows that the proposed approach attains an expiry-alert precision of 0.95 and recall of 0.93, and, over a six-month simulation, reduces expiry-related medicine wastage from roughly 12% to under 4% of stock. The principal contributions are an integrated serverless architecture for intelligent pharmaceutical inventory, an automated multi-condition alerting workflow, and an empirical demonstration that predictive, cloud-native management materially reduces waste and stock-outs while improving responsiveness.

Keywords: Inventory management; Expiry alert; Cloud computing; Serverless architecture; Healthcare informatics; Demand forecasting; Python; Automated notification