Risk-Based Predictive Maintenance in Medical Device Manufacturing Using Azure Databricks and SAP HANA

Abstract This paper introduces a risk-based predictive maintenance framework specifically tailored for medical device manufacturing operations within regulated healthcare environments. Utilizing Microsoft Azure Databricks for scalable machine learning and SAP HANA for real-time in-memory data processing, the solution analyzes IoT sensor data and maintenance logs to anticipate equipment failure. The framework adheres to FDA CFR Part 11 compliance, embedding features such as digital signatures, audit trails, and traceability. Results from a pilot implementation demonstrate a 28% reduction in unplanned downtime and 40% improvement in audit preparation efficiency. This white paper serves as a guide for technical professionals aiming to enhance predictive capabilities and regulatory readiness in smart healthcare manufacturing.