Keeping assets performing optimally requires structure, flexibility, and reliable data. This release introduces the Manage Reliability Cases app to centralize investigations, giving teams a clear view of alerts, operating conditions, and assigned actions in one place. The CAP-based recommendation apps are enabled with extensibility, allowing users to enhance the standard application with custom fields. You can now also assign multiple task lists as well as create standalone recommendations directly for industrial system elements. In asset health monitoring, we have introduced variant management to the Monitor Alerts app, and enabled curated visual inspection results to enhance condition-based monitoring by providing cleaner data for smarter, more accurate AI models.
Let's dive in to the highlights of SAP Asset Performance Management 2509!
Repeated equipment issues, such as high-temperature alerts on critical pumps, often require time-consuming investigations. Without a structured process, root causes may remain unclear, preventive actions may be missed, and reliability engineers spend valuable time chasing fragmented information.
The new Manage Reliability Cases application brings structure and visibility into the process. It centralizes investigations by linking alert analysis, operating condition reviews, and documentation of findings in one place. Reliability engineers can assign responsibilities, track actions, and ensure that recommendations are integrated with ongoing maintenance and risk management activities.
This provides the following benefits:
For more information, see the SAP Help Portal Manage Reliability Cases.
SAP Asset Performance Management (APM) is undergoing a significant transition to the SAP Cloud Application Programming (CAP) model. This transition aims to enhance the functionality, scalability, and maintainability of the APM applications. With release 2509, the Manage Recommendations and Implement Recommendations apps have transitioned.
To use these applications, you must have the role Recommendation_CAP assigned to your user. For more information on release timelines, support, and known limitations, refer to SAP Note 3603407.
The original recommendations apps have limited flexibility for custom or industry-specific data. The new CAP-based recommendations apps allow field extensibility to add custom fields.
For example (see screenshot above):
With CAP-based recommendations, organizations can unlock entirely new possibilities for proactive maintenance. Instead of being limited to static instructions, recommendations can now incorporate flexible conditions, tailored actions, and structured plans that evolve with operational needs.
Maintenance recommendations were previously limited to a single task list, making it hard to coordinate preventive and corrective actions and forcing teams to work around system constraints.
Now, multiple general maintenance task lists can be linked to any reactive or proactive recommendation. This allows teams to plan and coordinate complex maintenance activities directly in the system, reflecting real-world operational needs.
This brings the following benefits:
For more information, see the SAP Help Portal Creating a Task List for Recommendation.
Recommendations were previously limited to technical objects, making it difficult and inefficient to address systemic issues that affect multiple assets, entire processes, or plant-wide safety measures.
You can now create standalone recommendations directly for industrial system elements—just as for technical objects—without requiring an assessment. This enables a single, actionable recommendation to cover broader contexts, such as an entire production line or general operational best practice.
This enables the following:
For more information, see the SAP Help Portal Assigning an Industrial System Element to a Recommendation
To enable the enhancements to the recommendations mentioned above the OData API for Recommendation has been enhanced to:
For more information, refer to API for Recommendation.
As preparation for the transition to the Cloud Application Programming Model (CAP), the Risk and Criticality Assessment and Template GraphQL APIs have been deprecated. We recommend migrating to the Risk and Criticality Assessment OData V4 APIs, which align with CAP and deliver enhanced efficiency and capabilities. For further details, see SAP Note 3603407 and the official documentation Risk and Criticality Assessment ODATA V4 API and Risk and Criticality Assessment Template ODATA V4 API.
We’re excited to announce a major UI improvement in the Monitor Alerts app - Variant management is now available.
Organizations often have multiple units, such as maintenance plants and planning plants, each requiring a different perspective on alerts. With the new variant management, you can now filter alerts based on:
These options allow you to view only the alerts relevant to your role or unit, reducing noise and improving efficiency.
Standard Variant: A default, pre-configured view is available to all users.
Custom Variants: Apply your filters and save a variant with a new name—it automatically becomes your default.
Public Saving: Share your custom variant with the team so everyone benefits from the same view.
Tile Saving: Save a variant as a personal tile on your home page for quick access.
These features allow both administrators and individual users to create, share, and streamline their views, making monitoring and decision-making faster and more intuitive.
Looking ahead, we’ll also introduce attribute-based access control. This will allow organizations to restrict visibility of certain alerts based on planning units, ensuring users only see what’s relevant to their responsibilities.
Data inconsistencies and poor-quality inspection data reduce AI model accuracy. Additionally, managing and deploying multiple AI models across diverse scenarios requires high manual effort.
Within the recently introduced Manage Visual Inspection Results app, you can now curate data by adding or editing visual inspection results to resolve data quality issues. You also get access to detailed inspection reports, including a change log, enabling auditable workflows for AI model training.
This business value of this is:
Improved AI accuracy: High-quality inspection data enables reliable predictions.
Faster feedback cycles: Real-time result validation accelerates model refinement.
Confidence in AI decisions: Curated data supports trust and compliance in AI outputs.
See you soon for the 2510 update!
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