In my role as customer officer for SAP Intelligent Asset Management I am in close touch with many of our customers who are running various solutions to manage their assets. Common goals are to achieve high availability of their industrial equipment, ensure safe and resilient operations while managing the costs.
The operational maintenance teams typically have processes in place to manage activities such as equipment repair, performing corrective and preventive maintenance measures day by day. They have maxed out the capabilities for maintenance planning and execution within SAPs backend system of record with in-built integration along the process chains and the entire asset lifecycle from acquisition to dismantling.
At the same time teams of reliability engineers try to figure out on how to improve overall. How to save maintenance costs without increasing the risk of failure? These teams have processes and tools in place to run risk assessments like consequence analysis or to perform the 5-Why's, FMEAs (Failure Mode Effect Analysis) or methodologies for reliability centered maintenance. These time-consuming excercises which usually involve subject matter experts from many disciplines, are highly rated with fruitful insights followed by capturing forward-looking maintenance recommendations.
The crux of the matter is that the majority of valuable findings never come true as they are stuck in data silos. Either in spreadsheets or any other software environment which is somehow disconnected to the operational maintenance system.
So, what is the conclusion?
Well-known methodologies like FMEA or RCM (Reliability Centered Maintenance) help to balance and to define maintenance recommendations on all levels. You may assess the risk and criticality for an entire production line or a group of equipment. But also, you may put your equipment into the respective operational context, the designated function and evaluate potential functional failures to derive individual prescriptive maintenance recommendations.
Let us consider the solutions SAP Asset Strategy and Performance Management and SAP Predictive Asset Insights as one combined offering for holistic SAP Asset Performance Management going forward.
In order to reach optimal maintenance programs – with continuous improvement cycles – tight integration from SAP Asset Performance Management with SAP Enterprise Asset Management is paramount!
As you know, it is great to have the entire asset lifecycle strongly embedded within SAP Enterprise Resource Planning, form procurement to decommission and from design to operate.
At the same time SAP Asset Performance Management will nicely complement the SAP Enterprise Asset Management solutions with dedicated capabilities for strategy management, methodologies, IoT connectivity and machine learning for example to determine the machine health or the remaining useful life for a specific equipment.The Asset itself connects the two worlds together.
You can tell by the color coding whether the underlying solutions resides in SAP S/4HANA or with SAP Asset Performance Management on SAP Business Technology Platform. Key is strong integration of both sides!
Asset data are in the heart. Formerly known data silos like strategy plans on the one side and operational data on the other side, these data silos become history, eventually.
Both disciplines are about to work tightly together based on consistent and coherent asset data.
We will enable full integration for all process steps along the closed-loop going forward.
This typically starts with definition of maintenance strategy, followed by the implementation of the strategy – whereat dynamic rules will enrich conventional periodic maintenance plans. Then, IoT-real time monitoring with automated and rule based generating of maintenance demands. Also, the regular maintenance calls, the planning and execution of the respective activities.
The lower section of this loop is obviously conducted more often before precise analysis come to action. The analytical capabilities will tell exactly if and where strategies may need to be adopted. Ideally, the system will tell you about the insights based on data driven evidence. For example regarding bad actors, or equipment which are potentially over-maintained? Or you may think about smart system proposals to better balance equipment availability, maintenance costs and managing risk?
Let’s look into key business objects and respective data flows:
Beside the physical dimensions and structure – additional views are needed, e.g. for logical and functional views. We are currently working on this to make it happen in a coherent manner. Eventually, this will allow for example failure mode definition in a unique way for all disciplines! I think you get the point.
This is fundamental for all subsequent processes! First, you have the strategies defined and in place. Then, risk & criticality assessments help you to segment your Assets with indication for priorities. Methods like RCM and FMEA help you to determine the failure modes and their respective relevance. We learned from many customer projects about this diligent work and how time consuming these assessments are. We will ensure simplification in many ways, for example to apply assessments for an entire class of equipment, let's say convenient services to do the job.
Depending on desired function and the operational context, the underlying assessment may become very specific. All types of assessment will derive and propose maintenance recommendations.
The defined strategy is now designated to be implemented. We see the journey to predictive and finally prescriptive maintenance. The central objects with touchpoints and all potential combinations and strategy mix right in the middle.
The task lists and in some cases even inspection plans and checklists are defining "what" to be done. The answer to the question about "When" is twofold. You all know the maintenance calls defined and triggered via the maintenance plan for preventive maintenance. Well-defined and automated maintenance rules will allow dynamic and also more specific activities going forward. This way is very flexible, precise and effective to balance the overall challenge to maximize availability, at lowest cost by managing risks. This allows you data driven, condition-based forward-looking maintenance programs.
The maintenance backlog is unified and combines all maintenance demands together. It may include dynamic generated demands in addition to any kind of preventive maintenance as well as repairs, inspections and so on.
Again, the key element, some call it the heartbeat for maintenance, is the cognizant and coherent failure mode definition. We all know about the challenges technicians have when confirming their activities with accurate failure mode assignment. This is now simplified by the help of machine learning and will enable holistic processes as just described with the closed-loop.
Also, this will enable the feedback loop back to planning, for example to adjust recommendations based on profound failure mode analytics and forecasting capabilities.
Eventually, the entire system becomes intelligent.
Conventional KPIs like Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR) , will remain. Ultimately, you want to see and monitor how efficient and effective your overall maintenance program performs – as transparent as possible. Either for single assets, entire categories or asset classes. You may think of comparability with other plants and also learning and adopting insights to other plants.
This is what you expect from us and to achieve this, two topics are super important. Consistent data model across applications and secondly out-of-the-Box integration.
How to realize the end-to-end closed loop story?
This is exactly what we want to achieve using SAP One Domain Model – which is not specific just for asset management domain but all over SAP portfolio to be leveraged for tight integration across various solutions.
The graphics on the right hand side visualize the approach: All related solutions work and share core data which are unique. At the same time some solutions may need additional data for specific business objects and the need to manage these additional and specific data.
The semantics of the common core are the single truth for all interlinked solutions in the means of the data model. The core data are kept synchronous at all times!
This is key for our technical transformation going forward.
First, we follow SAP’s guidance to exchange data between applications:
Secondly, we evolve SAP Asset Strategy and Performance Management and
SAP Predictive Asset Insights into SAP Asset Performance Management
Let me recap and summarize key benefits for this product and technical evolution:
You may ask yourself when and how to embark on this exciting journey?
Our development teams are working full steam ahead to make this happen. Please stay tuned for announcements coming-up soon.
In the mean time you may want to look into the latest white paper for Intelligent Asset Management.
The current roadmap is here and specific note with latest information is published here.
The operational maintenance teams typically have processes in place to manage activities such as equipment repair, performing corrective and preventive maintenance measures day by day. They have maxed out the capabilities for maintenance planning and execution within SAPs backend system of record with in-built integration along the process chains and the entire asset lifecycle from acquisition to dismantling.
At the same time teams of reliability engineers try to figure out on how to improve overall. How to save maintenance costs without increasing the risk of failure? These teams have processes and tools in place to run risk assessments like consequence analysis or to perform the 5-Why's, FMEAs (Failure Mode Effect Analysis) or methodologies for reliability centered maintenance. These time-consuming excercises which usually involve subject matter experts from many disciplines, are highly rated with fruitful insights followed by capturing forward-looking maintenance recommendations.
The crux of the matter is that the majority of valuable findings never come true as they are stuck in data silos. Either in spreadsheets or any other software environment which is somehow disconnected to the operational maintenance system.
So, what is the conclusion?
Well-known methodologies like FMEA or RCM (Reliability Centered Maintenance) help to balance and to define maintenance recommendations on all levels. You may assess the risk and criticality for an entire production line or a group of equipment. But also, you may put your equipment into the respective operational context, the designated function and evaluate potential functional failures to derive individual prescriptive maintenance recommendations.
Let us consider the solutions SAP Asset Strategy and Performance Management and SAP Predictive Asset Insights as one combined offering for holistic SAP Asset Performance Management going forward.
In order to reach optimal maintenance programs – with continuous improvement cycles – tight integration from SAP Asset Performance Management with SAP Enterprise Asset Management is paramount!
As you know, it is great to have the entire asset lifecycle strongly embedded within SAP Enterprise Resource Planning, form procurement to decommission and from design to operate.
At the same time SAP Asset Performance Management will nicely complement the SAP Enterprise Asset Management solutions with dedicated capabilities for strategy management, methodologies, IoT connectivity and machine learning for example to determine the machine health or the remaining useful life for a specific equipment.The Asset itself connects the two worlds together.
You can tell by the color coding whether the underlying solutions resides in SAP S/4HANA or with SAP Asset Performance Management on SAP Business Technology Platform. Key is strong integration of both sides!
Asset data are in the heart. Formerly known data silos like strategy plans on the one side and operational data on the other side, these data silos become history, eventually.
Both disciplines are about to work tightly together based on consistent and coherent asset data.
We will enable full integration for all process steps along the closed-loop going forward.
This typically starts with definition of maintenance strategy, followed by the implementation of the strategy – whereat dynamic rules will enrich conventional periodic maintenance plans. Then, IoT-real time monitoring with automated and rule based generating of maintenance demands. Also, the regular maintenance calls, the planning and execution of the respective activities.
The lower section of this loop is obviously conducted more often before precise analysis come to action. The analytical capabilities will tell exactly if and where strategies may need to be adopted. Ideally, the system will tell you about the insights based on data driven evidence. For example regarding bad actors, or equipment which are potentially over-maintained? Or you may think about smart system proposals to better balance equipment availability, maintenance costs and managing risk?
Let’s look into key business objects and respective data flows:
Beside the physical dimensions and structure – additional views are needed, e.g. for logical and functional views. We are currently working on this to make it happen in a coherent manner. Eventually, this will allow for example failure mode definition in a unique way for all disciplines! I think you get the point.
This is fundamental for all subsequent processes! First, you have the strategies defined and in place. Then, risk & criticality assessments help you to segment your Assets with indication for priorities. Methods like RCM and FMEA help you to determine the failure modes and their respective relevance. We learned from many customer projects about this diligent work and how time consuming these assessments are. We will ensure simplification in many ways, for example to apply assessments for an entire class of equipment, let's say convenient services to do the job.
Depending on desired function and the operational context, the underlying assessment may become very specific. All types of assessment will derive and propose maintenance recommendations.
The defined strategy is now designated to be implemented. We see the journey to predictive and finally prescriptive maintenance. The central objects with touchpoints and all potential combinations and strategy mix right in the middle.
The task lists and in some cases even inspection plans and checklists are defining "what" to be done. The answer to the question about "When" is twofold. You all know the maintenance calls defined and triggered via the maintenance plan for preventive maintenance. Well-defined and automated maintenance rules will allow dynamic and also more specific activities going forward. This way is very flexible, precise and effective to balance the overall challenge to maximize availability, at lowest cost by managing risks. This allows you data driven, condition-based forward-looking maintenance programs.
The maintenance backlog is unified and combines all maintenance demands together. It may include dynamic generated demands in addition to any kind of preventive maintenance as well as repairs, inspections and so on.
Again, the key element, some call it the heartbeat for maintenance, is the cognizant and coherent failure mode definition. We all know about the challenges technicians have when confirming their activities with accurate failure mode assignment. This is now simplified by the help of machine learning and will enable holistic processes as just described with the closed-loop.
Also, this will enable the feedback loop back to planning, for example to adjust recommendations based on profound failure mode analytics and forecasting capabilities.
Eventually, the entire system becomes intelligent.
Conventional KPIs like Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR) , will remain. Ultimately, you want to see and monitor how efficient and effective your overall maintenance program performs – as transparent as possible. Either for single assets, entire categories or asset classes. You may think of comparability with other plants and also learning and adopting insights to other plants.
This is what you expect from us and to achieve this, two topics are super important. Consistent data model across applications and secondly out-of-the-Box integration.
How to realize the end-to-end closed loop story?
This is exactly what we want to achieve using SAP One Domain Model – which is not specific just for asset management domain but all over SAP portfolio to be leveraged for tight integration across various solutions.
The graphics on the right hand side visualize the approach: All related solutions work and share core data which are unique. At the same time some solutions may need additional data for specific business objects and the need to manage these additional and specific data.
The semantics of the common core are the single truth for all interlinked solutions in the means of the data model. The core data are kept synchronous at all times!
This is key for our technical transformation going forward.
First, we follow SAP’s guidance to exchange data between applications:
- Establish a coherent enterprise data model using SAP One Domain Model with common semantics for all key business objects in the Asset & Service Management domain
- The SAP One Domain Model for Asset & Service Management will be harmonized with the data model in SAP S/4HANA
- Enablement for data exchange via SAP Master Data Integration services which are based on the aligned domain models
Secondly, we evolve SAP Asset Strategy and Performance Management and
SAP Predictive Asset Insights into SAP Asset Performance Management
- SAP Asset Performance Management will evolve the business capabilities from SAP Asset Strategy and Performance Management and SAP Predictive Asset Insights based on aligned data model
- SAP Asset Performance Management architecture will adopt SAP One Domain Model for Asset & Service Management
- SAP Asset Performance Management capabilities on SAP Business Technology Platform will be integrated by design with Maintenance Management in SAP S/4HANA
Let me recap and summarize key benefits for this product and technical evolution:
- End2end process integration from asset strategy to maintenance execution
- Failure-mode centered and risk based strategies allow smart recommendations to continuously optimize all your maintenance activities
- Harmonized data models enable data driven insights and allow out-of-the-box integration
- Enablement for sophisticated analysis to monitor effectiveness of maintenance programs
You may ask yourself when and how to embark on this exciting journey?
Our development teams are working full steam ahead to make this happen. Please stay tuned for announcements coming-up soon.
In the mean time you may want to look into the latest white paper for Intelligent Asset Management.
The current roadmap is here and specific note with latest information is published here.
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