MedProcure: An AI-Enabled Hospital Procurement Management System with Multi-Level Approval Workflow

Hospital procurement involves purchasing medicines, equipment and consumables while keeping stock available, spending controlled and every decision accountable. In many hospitals this process still relies on manual requests, informal approvals and periodic stock checks. That leads to delayed purchases, understocking of critical items, overstocking that wastes budget, and poor traceability. To address these problems, MedProcure, a web-based Hospital Procurement Management System, has been developed. It manages the full procurement lifecycle: purchase request, configurable multi-level approval, purchase order, supplier response, goods receipt and inventory update. The system has a secure role-based access control mechanism, master data management for items, suppliers, manufacturers, categories, units and departments, and an approval matrix that routes each request by department, category and amount. The platform is built with Angular for the user interface and ASP.NET Core Web API for backend processing. Data is stored in a MySQL database accessed through Entity Framework Core, and JWT authentication protects the system. To make procurement more intelligent, the system is being extended with machine learning modules. An inventory understock and overstock analysis module uses XGBoost to classify stock status and recommend procurement actions. A seasonal demand prediction module uses the Temporal Fusion Transformer, a time-series model that handles seasonality and shows which variables influenced a forecast. A smart purchase request review module uses LightGBM to compare a requested quantity against current stock and recent consumption and then recommend an appropriate quantity. The AI modules only recommend, and the human approver keeps the final decision. Together, these features aim to reduce stockouts and excess inventory, speed up approvals and support better decisions in hospital supply management.