SAP Asset Performance Management (APM) is our solution to help S/4HANA and EAM customers develop maintenance strategies and manages asset health in a closed-loop business process. With APM, our customers can minimize the risk of asset failure and make faster, more-accurate maintenance decisions leveraging AI and sensor data.
Internet of Things (IoT) technology, therefore, is an integral part of our APM solution as it provides foundational capabilities to connect and manage devices, and to ingest sensor data for remote equipment monitoring and for AI and analytics. Across the globe and in all asset intensive industries, we continue to see our customers challenged to rationalize their various OT systems/layers and to collate their sensor data from disparate sources, in order to shift their maintenance approaches to be more condition-based and predictive.
To help our APM customers to accelerate and simplify this shift, SAP will embed Cumulocity IoT, a market-leading Industrial IoT platform, into SAP Asset Performance Management. With Cumulocity IoT, SAP Asset Performance Management aims to:
- Simplify connectivity scenarios; be able to connect to any IoT data sources including, smart equipment/sensors, edge devices, data historians/lakes, flat files etc
- Seamlessly manage IoT devices; manage your IoT devices within SAP APM using the embedded advanced device management and connectivity capabilities
- Continuously monitor asset health in a closed loop; detect anomalies and predict potential failures using AI and rule-based technologies in near-real-time using streaming IoT data to optimize maintenance activities
We also see the rise of artificial intelligence (AI) drives the adoption rate of IoT as AI helps generate invaluable insights from the large amount of sensor data. SAP Asset Performance Management uses SAP AI Core to detect anomalies in equipment behavior that will lead to potential failures, based on IoT sensor data. This anomaly detection capability is designed from the ground-up for reliability engineers, who are not data specialists or data scientists; they can quickly deploy this capability to continuously monitor equipment sensor data and generate alerts whenever the AI detects anomalies in its behavior, based on learnings from historical sensor data collected over time. Moreover, this AI-based predictive maintenance capability is applicable to various asset classes and equipment types as it would learn from customers own equipment data. Some of anomaly detection examples that we have seen in actual customer projects include:
- Filling valves on a beverage production line at a consumer goods manufacturer/retailer
- Air filters on transportation trucks at a mining operation
- Electric motor on a rotating equipment at an oil & gas company
To get a preview of this AI-based Anomaly Detection feature, which will be generally available in May, please take a look at this preview video.
As announced at the Hannover Messe, we plan to make Cumulocity IoT an embedded component in SAP Asset Performance Management later this summer. I will update this blog with the official Roadmap Explorer link once its updated there. In the meantime, please take a look at this preview video.
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