Predictive Maintenance

Monitor the performance of physical assets in the field and determine when maintenance of specific components is needed, resulting in higher uptime and less Service Level Agreement violations.

Maintenance jobs are traditionally scheduled at fixed intervals. With the rise of IoT technology, the behavior of physical assets can be tracked in real time. This results in much smarter maintenance plans, which leverage information about how an asset is used to schedule maintenance before components start to fail. This will minimize equipment downtime and maximize the component lifetime. The information can also be used to make sure field service engineers bring the proper components when they go onsite for maintenance, thus increasing the number of first time right visits. TimeSeries uses IoT technology such as MindSphere to predict when maintenance is needed, thereby maximizing the mean time between failures.

Production lines

For production lines, this means more predictable downtime, which can be taken into account when scheduling production and making promises to customers. Furthermore, manufacturers can now create new revenue streams where they can more reliably offer repairs and even complete revisions of delivered machines using IoT data. TimeSeries has helped several customers change their business model from only selling machines to full service organisations, taking ownership of the produced machine throughout the entire lifecycle.

Customer case

Liftinsight is a service provider focussing on all processes related to elevator management. The maintenance of the elevators at Liftinsight was scheduled to accommodate a certain amount of maintenance jobs per year. However, the problem with this model is that real maintenance does not depend on time, but on actual usage of the elevator.


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