Orchestrating Industrial Sensor Analytics and Equipment Degradation Forecasting Across SAP Plant Maintenance and Connected IoT Infrastructure
Industrial asset management requires effective methods to identify equipment deterioration, prevent unexpected failures, and minimize maintenance costs. Integrating Internet of Things (IoT) technologies with SAP Plant Maintenance enables continuous equipment monitoring and data-driven maintenance decisions through predictive machine learning models. This study develops an integrated framework connecting sensor data acquisition, equipment condition assessment, failure prediction, and enterprise maintenance workflows. A quantitative research approach evaluates historical maintenance records, sensor measurements, equipment operating parameters, and failure patterns using supervised machine learning algorithms. Model performance is assessed through precision, recall, F1-score, and prediction accuracy, while operational effectiveness is measured through unplanned downtime, maintenance expenditure, equipment availability, and mean time between failures. Proposed evaluation targets include predictive accuracy above 90%, a 20% reduction in unplanned downtime, a 15% decrease in maintenance expenditure, and a 10% improvement in equipment availability, subject to validation against baseline performance. Predictive outputs are integrated with SAP Plant Maintenance notifications, inspection priorities, and work order planning to support timely maintenance interventions. The framework aims to overcome limitations associated with reactive maintenance, isolated sensor information, and rigid service schedules. Its principal innovation lies in connecting predictive analytics with established enterprise asset management processes through a structured IoT integration approach. The findings are intended to establish measurable evidence of operational improvement while examining data quality, sensor interoperability, and model reliability. This research contributes to a practical evaluation framework for industrial organizations seeking to improve equipment reliability, optimize maintenance resources, extend asset service life, and strengthen operational continuity.
Keywords: Industrial IoT, SAP Plant Maintenance, Predictive Maintenance, Machine Learning, Sensor Analytics, Equipment Degradation Forecasting, Condition-Based Monitoring, Predictive Failure Analysis, SAP GRC, Cyber Attack Detection, SAP IAS/IPS, Industrial Asset Management, Equipment Reliability, Anomaly Detection, Sensor Data Integration, Maintenance Optimization, Failure Prediction Models, Industrial Cybersecurity, Real-Time Monitoring, Enterprise Asset Management, Data-Driven Maintenance
