Leveraging Hybrid Edge-Cloud Predictive Maintenance in Pharmaceutical MES: An Industry 4.0 Approach Using Big Data

Abstract

The pharmaceutical sector relies on stringent manufacturing environments to safeguard product integrity and uphold regulatory standards. Unexpected equipment failures can lead to costly downtime, regulatory exposure, and compromised quality. To address these challenges, this paper presents an integrated Hybrid Edge-Cloud Predictive Maintenance (HEC-PdM) framework embedded within a Manufacturing Execution System (MES). By combining edge computing for real-time anomaly detection with cloud-based machine learning (ML) analytics, manufacturers can transition from reactive to predictive and prescriptive maintenance strategies. The methodology includes data collection and preprocessing at the edge, federated learning in the cloud, and seamless MES integration to automate maintenance workflows and compliance documentation. Case studies highlight significant benefits, such as a 45% reduction in maintenance costs, minimized downtime, and improved production quality. Finally, the paper discusses future directions, including enhanced security protocols for federated learning, self-adaptive AI systems, and quantum ML to further address the complexities of pharmaceutical manufacturing.