Regular preventative maintenance has been a semi-science balancing objective and subjective consideration when determining when service is required. The next step is predicting when repairs need to be made based on machine indicators and projected issues. Industry 4.0 technologies help supply the necessary big data for analyses. The SAP predictive maintenance solution, currently known as SAP Predictive Asset Insights (SAP PAI), combines operational technology data (sensor data) and information technology data (maintenance data) to create digital insights for industrial assets.
Asset management and maintenance professionals customers can use the solution to predict equipment degradation and malfunction through advanced analytics (machine learning based), the Internet of Things (IoT), machine learning, and engineering (physics-based) models to deliver decision support. Some of the benefits include implementation of a data-driven, condition-based approach; increased asset availability; and, optimized maintenance effectiveness. However, it can be difficult to launch a program from scratch. On this page, we provide some helpful resources for your journey. Below is a link to our guide that reviews what information you will need during the planning process.
Detecting system malfunctions using real-time fault management
Optimizing asset optimization and reliability
Improving quality and service by predicting malfunctions
Offering performance-based service and dispatch proper technical assistance
Reducing maintenance costs
SAP Predictive Maintenance (SAP PAI)
Cloud and on-premise deployment
Leverage sophisticated predictive modeling and analysis tools – including statistical modeling, data discovery, asset scoring, and data visualization.
Insight from sensor data
Monitor connected devices and support IoT data transfer services to optimize data management with scalable and cost-effective storage for time-series data.
Integration with SAP ecosystem
Connect with SAP enterprise solutions – such as SAP S/4HANA – and third-party maintenance execution systems.
5 Steps to Consider Before Launching Your Predictive Maintenance Program
Preventative maintenance has been a semi-science balancing objective and subjective considerations when determining when service is required. However, the cyber-physical connections of Industry 4.0 make data available. This information is fed into sophisticated machine learning algorithms for processing, which then drives predictive maintenance decisions. Yet, there are a few prerequisites in order to prepare your operations for it.
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