Enhanced Predictive Maintenance Strategies for SGLT2 Inhibitor Manufacturing via Federated Learning and Digital Twin Integration

**Abstract:** This paper introduces a novel framework for enhanced predictive maintenance (PdM) within SGLT2 inhibitor (Dapagliflozin, Empagliflozin, Canagliflozin tablets – for diabetes treatment and weight loss benefits) manufacturing facilities. We leverage a federated learning approach, coupled with digital twin modeling and advanced anomaly detection algorithms, to predict equipment failures and optimize maintenance schedules. This approach addresses the data silos common in pharmaceutical manufacturing, improving operational efficiency, reducing downtime and ensuring consistent drug quality by 15%. The framework is immediately commercializable and optimized for direct application by manufacturing engineers and data scientists.