With this release of SAP Asset Performance Management, we’re continuing to deliver on the feedback and improvement requests we hear from our customers and partners every day.
Several of the innovations highlighted in this update were directly influenced by customer feedback and SAP Continuous Influence requests — including the new Industrial System Category configuration capabilities, one of the most highly requested enhancements in this area, and the hotly anticipated AI-powered anomaly detection,
If you’d like a broader look at what’s planned for the second half of the year, join me and the APM product management team as we present our upcoming SAP APM roadmap webcast on June 3rd at 4pm CET, where the we'll share upcoming innovations and answer your live questions. Register here.
More Flexible Industrial System Modeling with Industrial System Categories
What if your Industrial System structure could follow a standard where needed, but still adapt to the way your business actually models assets?
With the new Industrial System Category configuration, SAP Asset Performance Management gives customers more control over how Industrial Systems and Industrial System Templates are structured, from aspect relationships to technical object assignments, including ISO-aligned and customer-specific modeling rules.
Challenge
Industrial System modeling needs to support different customer requirements. Some customers want to follow a standard-aligned structure, while others need more flexibility to reflect their own asset structures and modeling rules.
Previously, fixed modeling behavior made it harder to adapt Industrial Systems to customer-specific requirements, especially when defining relationships between aspects such as function, product, and location, or when assigning technical objects such as equipment and functional locations.
Solution
With Industrial System Categories, users can define how Industrial Systems and Industrial System Templates should be structured.
Categories make it possible to configure:
- Relationships between aspect types
- Which technical objects can be assigned
- Whether single or multiple assignments are allowed
- Reference Designator System formatting
- Whether standard-compliant behavior should be applied
SAP provides two pre-shipped categories:
Default ISO category
For structures that follow the ISO 81346-compliant standard. This category applies stricter modeling rules, such as allowing assignments only to the Product Aspect and only one instance of equipment or functional location.
Non-default ISO category
For existing or more flexible structures that follow parts of the ISO-related modeling approach, but not the stricter default setup. Existing Industrial Systems and Industrial System Templates are automatically assigned to this category during upgrade, so they continue to work as expected.
Customers can also create their own categories. For example, a customer can allow equipment to be assigned to a Function Aspect, functional locations to a Location Aspect, and define whether multiple assignments are allowed.
Once a category is active, it can be used when creating Industrial Systems or Industrial System Templates. If marked as default, it is proposed automatically, while users can still choose another active category if needed.
Outcome
Industrial System Categories help customers model systems in a way that better fits their business, while still supporting governance and standard alignment.
This gives customers:
- More flexible Industrial System modeling
- Support for ISO-aligned and customer-specific structures
- Better control over aspect relationships and technical object assignments
- Smooth upgrade behavior for existing Industrial Systems
- Consistent modeling rules across Industrial Systems and Templates
In short, customers no longer have to choose between standardization and flexibility. With Industrial System Categories, they can support both. See it in action in this video:
and see the SAP Help Portal Industrial System Category.
Field extensibility for the “Explore Technical Objects” app
For many customers, critical asset information is stored in custom ERP fields tailored to their business processes. But without extensibility support in APM, that information remains disconnected from reliability analysis and day-to-day decision-making.
With the new field extensibility capabilities for the Explore Technical Objects app, customers can now bring customer-specific ERP fields directly into SAP Asset Performance Management and make them available across Technical Object details, filters, and list views.
Challenge
The Explore Technical Objects application previously did not support extensibility for customer-specific fields.
As a result:
- Custom fields from SAP S/4HANA or SAP ERP could not easily be replicated into APM
- Reliability Engineers could not view customer-specific business attributes within the application
- Important operational context remained separated from APM processes and analysis
This made it harder for customers to tailor APM applications to their own business requirements.
Solution
The new extensibility capabilities introduce extension points that allow custom fields from SAP S/4HANA or SAP ERP to be integrated into APM through the Integration Layer.
The solution includes:
- Extensibility support at CDS view level
- SAP CAP framework extensibility capabilities
- Key User Extensibility support for Technical Object applications
- Support for customer-specific fields in detail pages, filters, and list views
This allows customers to seamlessly integrate additional ERP business attributes into the APM data model and expose them directly within the Explore Technical Objects app.
Outcome
With field extensibility, customers can now enrich Technical Object information with business-specific ERP data and make that information available directly within APM applications.
This provides:
- Better visibility into customer-specific business context
- More flexible Technical Object analysis
- Extended filtering and list reporting capabilities
- Reusable custom fields across multiple APM applications
- Improved business decision-making by combining standard APM data with customer-specific ERP information
For Reliability Engineers and business users, this means working with a more complete picture of the asset without needing to switch between systems.
Watch this video:
For more information, see the SAP Help Portal Replicating Custom Fields of Explore Technical Objects from SAP Backend Systems.
Anomaly Detection (Early Adopter Access)
This month's release brings one of the most anticipated additions to SAP Asset Performance Management: Anomaly Detection. This new capability, currently available through Early Adopter Access, marks a significant step forward for condition-based maintenance — and it doesn't require AI expertise to use. Let's take a closer look.
Note: Details on how to get Early Adopter access are described on the SAP Asset Performance Management Help Portal .
AI-Powered Anomaly Detection for Condition-Based Maintenance
Challenge
Asset health is rarely a single-sensor problem.
In practice, monitoring an asset's condition means working with many indicators — temperature, vibration, pressure, flow rates — all of which vary depending on the operational context. When that context changes frequently, defining static threshold rules becomes difficult, if not impossible.
To make things harder:
- A single indicator is rarely enough to reliably signal a developing fault
- The operational context of an asset can shift frequently, making fixed alert thresholds unreliable
- Known failure events are rare, which makes it hard to distinguish normal from abnormal behavior across a fleet
Traditional rule-based monitoring approaches can handle simple cases, but when asset behavior is multivariate, dynamic, and context-dependent, rule definitions quickly become unmanageable.
Solution
With the new Anomaly Detection capability in SAP Asset Performance Management, business users — without any AI expertise — can train anomaly detection models in a few guided steps.
The Manage Anomaly Detection Models application uses the unsupervised Isolation Forest algorithm to identify unusual patterns across multivariate sensor data streams. Here's what makes it practical:
- Notification-guided training: Existing maintenance notification data can be used to identify known failure periods and select the most relevant training data — so the model learns what “abnormal” looks like with precision.
- Flexible training configuration: Define up to 20 training periods per model, with time-based and indicator value-based exclusions to filter out irrelevant data (maintenance windows, operational context changes) and sharpen detection accuracy.
- Fleet-level models: Train a single model across multiple assets that share similar attributes and operational context, enabling consistent monitoring across an entire asset fleet.
- Simulation before deployment: Once trained, simulate the model's performance on historical data. Adjust the anomaly score threshold interactively to find the right balance between sensitivity and false positives — before releasing the model into production.
- Seamless integration with monitoring rules: Released models feed directly into anomaly detection scoring rules and monitoring rules, generating anomaly scores that trigger alerts and notifications when defined thresholds are exceeded.
Outcome
Anomaly Detection transforms how you approach condition-based maintenance in SAP APM:
- Implement condition-based maintenance strategies where rule-based monitoring would fail or be impractical to configure
- Detect early signs of asset degradation across many indicators simultaneously
- Identify bad actors in an asset fleet and act before failures occur
- Reduce manual IoT data analysis — the model handles the pattern recognition
- Optimize maintenance schedules by performing work only when the asset's condition warrants it
For more information, refer to the SAP Help Portal — Anomaly Detection. Watch this space for a dedicated enablement blog and webcast coming soon.
Bringing Maintenance Context into Indicator Monitoring
What good is an indicator chart if it only tells part of the story?
Challenge
Previously, Reliability Engineers monitoring asset health through indicators and condition data often had to switch between multiple applications to understand what actually happened around a spike, drop, or anomaly.
Maintenance notifications and work orders existed — but not in the same context as the indicator trend itself.
As a result:
- Notifications and work orders were not visible directly within the indicator chart
- Users had to navigate across multiple applications to investigate issues
- Correlating maintenance activity with indicator behavior was more time-consuming
- Important operational context could easily be missed
This created fragmented visibility between condition monitoring and maintenance execution, making investigations slower and less efficient.
With the latest enhancement, maintenance notifications and work orders can now be viewed directly alongside indicator trends, helping Reliability Engineers connect asset behavior with maintenance activity more quickly and efficiently.
Solution
The enhanced indicator monitoring experience now integrates maintenance context directly into the indicator chart.
Notifications and work orders can be visualized alongside indicator trends, helping users understand:
- When maintenance activities occurred
- Whether actions were triggered by condition changes
- How maintenance execution relates to asset behavior over time
By combining operational and maintenance context in a single view, users can investigate asset events more efficiently and with fewer navigation steps.
Outcome
With maintenance information embedded directly into indicator monitoring, Reliability Engineers gain a more connected view of asset performance and maintenance history.
This helps customers:
- Improve correlation between asset health and maintenance actions
- Accelerate root cause investigation
- Reduce context switching between applications
- Make faster and more informed maintenance decisions
- Gain better visibility into the effectiveness of maintenance activities
Instead of analyzing trends and maintenance activities separately, users can now view them together — in context.
See you for the next update!
