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In a soon to come upgrade of SAP Asset Performance Management (APM), we are thrilled to introduce a new feature: Anomaly Detection. This will transform how reliability engineers and business users leverage advanced AI technologies in their daily work.

In this blogpost I would like to cover why AI-based Anomaly Detection is important for the industry, and why this is an important investment for SAP. In future blogposts we will cover some typical use-cases as well as some more details on the solution itself.

Why is AI based Anomaly Detection important in industrial asset management?

Industrial asset management aims to ensure operational continuity and minimize downtime, as unexpected equipment failures will lead to operational disruptions, financial losses, and safety risks. Anomaly Detection, using advanced AI to analyze real-time IoT sensor data, forms the foundation of preemptive maintenance strategies by identifying potential issues before they escalate. This intelligence layer transforms raw data into actionable insights for optimal maintenance scheduling, enhancing asset reliability and life, and managing maintenance costs. Anomaly Detection fosters a smarter, efficient approach to asset management that meets the modern industrial landscape's needs for reliability, safety, and profitability.

The Perfect Solution for the Reliability Engineer

Our anomaly detection feature is designed with the reliability engineer in mind – a vital role in guaranteeing optimal operational uptime and efficiency for an organization's infrastructure and assets. By contrast with other market solutions demanding specialized technical skills, our solution empowers everyday business users. They can harness cutting-edge technology in routine tasks, simplifying operations that used to necessitate considerable manual work.

From Customer Request to Feature in SAP Asset Performance Management

Acknowledging the high demand from our customers for Anomaly Detection, we have taken significant steps to address this requirement. SAP Asset Performance Management (APM), evolving from SAP Predictive Asset Insights (PAI), now comprises this in-demand feature, thereby ensuring a smoother transition from PAI to APM. This is a testament to SAP's dedication to actively listening to customer feedback and continuously enhancing our solutions, as well as extending the functionality of our solution.

Harnessing AI and Machine Learning for Enhanced Condition Monitoring

Using machine learning technology, Anomaly Detection can automatically identify and warn of potential machine failures by analyzing IoT sensor data. This approach is not only generic and applicable across a wide range of machine types and signal types but also does not require users to have in-depth knowledge of data analysis. The feature effectively supports maintenance decisions through automatically triggered alerts, significantly reducing the need for manual data inspection.

Streamlining and Automation

The implementation of Anomaly Detection in SAP Asset Performance Management represents significant business value by automating the process of recognizing inconsistent patterns in sensor data streams. This greatly reduces human effort and integrates the feature with alerting functionality in APM to warn users about potential issues with machines in operation. The result is more efficient follow-up on analysis results and a reduction in manual workload.

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A video describing the new functionality can be found following this link.


The Anomaly Detection feature in SAP Asset Performance Management 2404 is more than just a technological innovation; it is a strategic investment in the future of maintenance and reliability. By making advanced AI technologies accessible to business users, SAP sets a new standard for how companies can monitor and maintain their valuable assets. We look forward to seeing how our customers will benefit from this feature, and we are committed to continuing to deliver solutions that meet and exceed their expectations.

Please check the roadmap for SAP Asset Performance Management to find out about upcoming AI features.

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