Periodic Automatic Replenishment (PAR) is an inventory management policy that assists the healthcare sector in keeping the right amount of stock on hand to avoid excess stock and the potential for products to expire. Traditionally, hospitals rely on the experience and firsthand knowledge of stock management technicians to keep their store supplies with enough equipment. However, manual management based on 'gut feeling' and/or nursing feedback may lead to missing products or incorrect stock orders. Extracting accurate data is often too complex or time-consuming, resulting in a lack of critical reporting data to manage product inventories across the organization properly. However, adopting forecasting techniques and incorporating them into traditional PAR management policies can provide efficient solutions for controlling hospital warehouse inventory at the lowest cost. We have proposed a deep learning-based framework to monitor inventories to reduce costs and variation, create efficiencies, and improve the quality of patient care in hospitals. Furthermore, the proposed forecasting framework's performance is assessed using a real-world scenario within a hospital. The findings indicate that the system is capable of accurately tracking the usage of medical equipment, which results in a significant reduction in the unavailability of assets.