Machine Learning-Based Predictive Maintenance Framework for SAP in Manufacturing

Abstract

Predictive maintenance (PdM) in the manufacturing sector has become critical in enhancing operational efficiency, reducing downtimes, and optimizing maintenance schedules. Traditional maintenance approaches are often reactive or preventive, leading to resource wastage and unforeseen equipment failures. This study proposes a machine learning-based predictive maintenance framework integrated with SAP systems specifically tailored for manufacturing environments. By leveraging SAP’s vast data handling capabilities combined with machine learning algorithms, our framework predicts potential equipment failures based on historical data and real-time metrics. Key results indicate that this approach can significantly reduce maintenance costs, improve asset lifespan, and decrease unplanned downtimes. This manuscript discusses the methodology, model development, evaluation, and implications of implementing a predictive maintenance framework within SAP environments for manufacturing.