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Author's profile photo Gabriele Pfaffmann

Deepen Insight into Your Assets using the Internet of Things

The idea of predictive maintenance is not new. However, automating predictive maintenance using internet-connected sensors and big data analytics is, and it is transforming the way businesses operate. Predictive maintenance not only benefits asset owners. It creates new opportunities for service providers and manufacturers as well.

  • Asset owners benefit by being able to analyze trends, better understand defect and failure patterns, and minimize downtime so that they get the most operational value out of equipment assets.
  • Service providers can expand service offerings to asset owners and deliver standard and add-on services more quickly and efficiently.
  • Asset manufacturers can identify design issues more quickly, lower warranty costs, predict recalls, and create innovative aftermarket business models.


Realizing these benefits depends on capturing data from many sensors built into the equipment and analyzing it in the context of a business objective. That is exactly what the cloud edition of SAP Predictive Maintenance and Service does.

One of the greatest challenges to optimizing productivity in asset-intensive operations has always been asset maintenance and unexpected break-downs. To avoid catastrophic failures, operators create maintenance schedules for critical equipment. The equipment is periodically shut down, and maintenance personnel go through it to perform a checklist of routine preventive maintenance tasks. The machine is out of service during maintenance, and knowing this, businesses build production workflows around machine down-times. And then there are unexpected breakdowns which can seriously disrupt work schedules.

Predictive maintenance and service using internet-connected sensors built into the machines is completely changing traditional machine maintenance practices. The key technologies behind IoT based predictive maintenance and service are:

  • Machines built with hundreds or even thousands of internet-connected sensors. These sensors typically monitor temperature and vibration on key components inside the machine.
  • A software solution, like SAP Predictive Maintenance and Service, which captures data from all the sensors and performs near-real-time analysis to determine when components are at risk of failure. The software platform is capable of simultaneously processing the operational characteristics of thousands of machines, each equipped with its own sensor array.

This approach to predictive maintenance and service makes it possible to avoid unnecessary preventive maintenance and significantly shortens maintenance cycle times. It also greatly reduces the chances of untimely break-downs. This means businesses operating machine-intensive processes realize more value from their equipment investments. It also gives service providers and manufacturers new opportunities to change their traditional business models.

SAP and GEA Collaborate to Help Companies Improve Operational Excellence with the Internet of Things

Here’s an example. GEA, one of the world’s largest providers of food processing equipment, and SAP have just announced GEA’s intent to use the SAP Predictive Maintenance and Service solution, cloud edition, to help GEA’s remote service technicians monitor the status of machines located at GEA customer sites and identify unusual trends or machine behavior in near-real-time. Key things to note about this announcement are that the service technicians can monitor equipment remotely without having to be on the customer site. This makes monitoring and maintenance practical even when equipment is located in inaccessible places, and it enables more efficient scheduling of local service providers. The other thing to note is that the analysis happens in near-real-time, which makes it possible to predict maintenance problems and optimize maintenance in ways that minimizes the impact on normal business operations. With this capability, GEA plans to offer new kinds of services agreements and make new commitments about machine performance. With the cloud based system, GEA can easily expand the program to cover more equipment.

How big of a difference can IoT based predictive maintenance and service make?

Organizations using this approach have shown a 44 percent drop in unplanned down time, 17 percent lower annual maintenance costs, and a 28 percent greater return on equipment assets (Source: SAP Performance Benchmarking).

To learn more about SAP Predictive Maintenance and Service, join us at SAPPHIRE NOW in Orlando, May 5-7 and attend informative sessions like this one: Reduce Asset Downtime Through Predictive Maintenance. So start putting together your personal agenda today or check out the Sample Agenda for Asset Management, which is a recommendation to help you identify key sessions for Asset Management.

More information on SAP Predictive Maintenance and Service:

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      Author's profile photo Former Member
      Former Member

      good one