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FAQs
SAP Asset Performance Management
The following FAQs are based on the most frequently asked questions asked during our webinars on SAP Asset Performance Management. They provide a concise overview of the aspects that participants are particularly interested in.
Webinar | What’s New in APM? – Updates 2024/2025
New features, strategic enhancements, and a clear vision for the future—experience how SAP Asset Performance Management (APM) is redefining your maintenance processes. Learn more!
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To the webinar recording
The implementation time depends on the complexity of the image analysis and the available image material. Assuming that sufficient high-quality image material is available for training the AI model, a runtime of approximately three months can be expected for a prototype. The technical objects must be available in APM (synchronization from an S/4 system) so that they can be enhanced with visual indicators. The AI model is connected via standard APIs.
Yes, theoretically you could define your own aspects, but then you would have to forego the standardized industry reference architectures and build your own reference architecture. Subtypes of the aspects function, product, and localization would be conceivable. However, we would recommend first mapping the system in standardized reference architectures.
The evaluative AI is not part of SAP APM and must be set up separately. Customers are free to choose where the AI model is trained and hosted; the connection is established via APIs provided by SAP.
We have already implemented projects in which vibration analysis evaluations were used to monitor asset health. This knowledge should also be transferable to SKF vibration analysis evaluation. The approach depends on how intelligent the system implemented by SKF already is.
1. Smart sensors from SKF already evaluate health status and vibration analyses. These analyses can then be transferred to SAP APM via machine alerts, triggering maintenance requirements.
2. If the sensors are not intelligent, or the evaluations of the intelligent sensors are insufficient, the sensors can be connected directly to SAP APM via OPC Gateway or indirectly via IoT platforms/production systems. Monitoring and its rules, anomaly detection, etc. are then defined in SAP APM.
In the first case, too, the sensor values from SKF vibration analysis could be transferred in their entirety to SAP APM if their components have been installed in more complex systems and these more complex systems contain additional sensor systems that are relevant to the overall asset health status.
An FMEA does not include a risk and criticality analysis. However, the risk and criticality analysis is usually performed at the asset level prior to the FMEA.
The FMECA contains a normal FMEA and evaluates the risk and criticality of the failure modes. Instead of just obtaining an RPZ according to the FMEA methodology, I can assess the failure mode using a specially defined RCA. This allows the criticality of the failure modes to be further quantified.
New best practice processes:
1. Process for analyzing equipment failures and reliability
2. Process for defining and optimizing the maintenance strategy
3. Process for implementing recommendations
4. Monitoring the health status of equipment
New best practices A2D – Acquire to Decommission: me.sap.com/processnavigator/SolS/EARL_SolS-059/2023-FPS01/SolP/SP-3160
By implementing these processes, you can map the closed loop fully integrated in SAP.
Introduction to APM:
First, I recommend checking the extent to which the business process is already in place.
1. Some companies do not yet have business processes for APM, i.e., for asset health and/or asset strategy processes.
2. Business processes usually have a longer ramp-up than the implementation of APM system processes. These are usually set up more quickly (a few weeks to months).
I recommend an empirical approach: pilot on a critical asset where the processes are implemented (e.g., RCA, FMEA, CBM, recommendation implementation). I then recommend obtaining commitment from management and the business for the defined scope and setting up a cross-functional team (reliability engineer, APM/S4 specialist, functional and technical, IoT specialist, development engineer).
SAP resources:
SAP has updated the APM Learning Journey in the Learning Hub. I would recommend this to anyone interested in getting started.
Learning Journey SAP APM: learning.sap.com/learning-journeys/managing-sap-asset-performance-management
SAP user groups are currently very active, and customer engagement initiatives are regularly created, e.g., DSAG.
No, not in the standard version. The use case would need to be discussed in more detail. An API for FMEA is promised in the roadmap for Q2/2024. It would be technically possible to adjust the risk.
The standard version does not yet include inheritance functions for risks, and the inheritance logic quickly becomes very complex in detail. My recommendation would be to start with a risk assessment using RCA at a higher system level. Future RCAs can then take place at a lower system level.
Currently, SAP does not support user-defined methods and does not have them on Roamdap. It would be conceivable to use RCA and an RCA template.
Depending on the use case, the rule engine is recommended for calculating the reliability analysis, where system events could be imported into the rules as machine alerts. Alternatively, an analysis can be checked in the analysis dashboard of the embedded analytics function (SAC).
In FMEA, catalogs of functions and function failures are optional. In RCM, they are mandatory. Finding a recommendation is methodically standardized in RCM through catalogs, while in FMEA, finding a recommendation is left to the user and is not methodically supported. In RCM, a recommendation for the maintenance strategy (also reactive) is given for each failure mode, whereas in FMEA, recommendations are only given if the RPZ is higher than, for example, 100. In RCM, there is no evaluation of the RPZ; instead, it is methodically supported by answering questionnaires.
The embedded version is available with the APM license; no separate license costs are necessary. The embedded version has limited functionality; for example, planning and machine learning functions are not available. The data objects are limited to those relevant to the environment, such as technical objects, messages, alerts, classes, characteristics, etc. IoT sensor data is provided in aggregated form.
Full integration with S/4 HANA 2021 FPS01 and higher, as well as S/4 Cloud. Integration with minor restrictions possible with S/4 HANA 2020 and ERP.
Customer Influence, integration of Python into the rule engine. According to Roadmap, this feature is planned for Q2 2024.
