SAP Predictive Maintenance and Service, cloud edition is one of multiple products that make up and enable SAP Intelligent Asset Management (IAM). Each IAM product covers a wide range of capabilities by capitalizing and enhancing the modern data sets that are available to Enterprise Asset Management (EAM) customers.
By converging operational data with IT business data, SAP Predictive Maintenance and Service, cloud edition aims to provide new insight into how an asset is performing where maintenance tasks can be moved up to avoid failure or pushed back to avoid cost; this enables the reduction in risk, and maintenance spend with an increase in asset availability.
Moving from Preventive to Predictive Maintenance
Currently, most EAM-centric organizations utilize preventive maintenance to ensure assets are in working order and to avoid sudden breakdown surprises. That serves a good purpose—if an asset breaks down suddenly, the cost goes well beyond the fix, it includes possible operational loss and revenue loss as well.
However, with the advancement of data storage and IoT with sensor technology, we now know more about asset performance on an individual basis, rather than expected performance from the manufacturer. With that more specific asset information, we can predict when each asset will need repair and recuperation.
This is more efficient than preventive maintenance, because it avoids unnecessary maintenance activities. If one of your assets is performing beyond the manufacturer’s expectation, taking a preventative approach may lead you to fixing something that really isn’t broken.
With predictive maintenance, your asset will tell you when it needs to be fixed through sensor data combined with contextual information from other business data sources. That leads to a more optimized approach that is likely to save your organization time and money.
How Does Predictive Maintenance Work?
SAP Predictive Maintenance and Service provides insight into asset performance with the ability to predict potential failure and analyze root cause by applying machine learning to sensor data, combined with available contextual data.
The solution also provides a 360-degree view of each asset, also known as a digital twin. This means all the information related to your asset—model, master data, transaction, and performance, can be viewed in a single location. SAP Predictive Maintenance and Service also includes advanced analytics features to support maintenance strategy and execution.
The analytics component includes failure mode analytics which uses machine learning to generate KPIs around documented failure modes. There are also IT/OT fusion views, which combine geospatial with sensor data to help you prioritize which assets are most in need of attention. Advanced rule-based alerts can also be created for maintenance professionals, and Fingerprint Comparison gives a visual-based comparison of asset reference states.
Ensuring Trains Run on Time
One train operator has used SAP Predictive Maintenance and Service to move from preventive maintenance to predictive recently, and it expects great results. In the past, all maintenance on trains was based on the mileage the trains had traveled. This, of course, assumes that all miles are created in equal when it comes to wear and tear.
With predictive maintenance, the train operator uses energy dissipation instead of mileage to monitor brake systems and uses open/closure cycles and times to monitor doors. The has led to higher asset availability—the train operator isn’t pulling trains out of service to make repairs that aren’t necessary, and predictive issues before they arise. That asset availability in turn has meant higher passenger satisfaction. Everyone is happier when the train works and is on time.
The train operator projects over $100 million in maintenance cost savings through using SAP Predictive Maintenance and Service.
Predictive is the Future (and the Present)
Preventive maintenance has served EAM organizations well, but with predictive maintenance we know we can do even better and save maintenance costs while extending asset life in the process.