Automating medical device warehouses: strategies for medium to large volume

Purpose- The study explores automation strategies in medical device warehousing, where efficiency, accuracy, and regulatory compliance are critical. It focuses on leveraging machine learning and advanced technologies to optimise inventory management, quality control, and storage processes in medium- to large-volume operations. Methodology- A large-scale dataset was analysed using machine learning models, including XGBoost and Long Short-Term Memory (LSTM), for demand forecasting. Feature engineering and rigorous model evaluation were applied. Automation technologies such as Automated Storage and Retrieval Systems (AS/RS), collaborative robots (cobots), and IoT-enabled inventory tracking were integrated into workflow optimisation. Findings- The models demonstrated strong performance in forecasting demand and supporting automated processes. Results showed improved operational efficiency, enhanced inventory accuracy, and reduced labour costs, highlighting tangible financial and logistical benefits for medical device warehousing. Conclusion- Machine learning and automation provide transformative solutions for medical device warehouses, enabling regulatory compliance, cost efficiency, and scalability in high-volume environments Keywords: Medical device warehousing, warehouse automation, robotics, inventory management, compliance, machine learning models