Repairing a device once it’s ‘broken’ can lead to unpredictable and expensive downtime. Scheduled maintenance improves the situation, but a static maintenance schedule can cause unnecessary interruptions. In the worst case, required parts need to be ordered, extending downtimes significantly. Predictive maintenance solves these issues:
- Combining live sensor data and past experience enables prediction of necessary maintenance activities and schedules. Production processes can then be adapted to synchronize and integrate maintenance activities with the least possible impact.
- Predictive maintenance data can be live information regarding the product quality that the devices produce. Maintenance for devices that produce lower quality batches may be delayed in this scenario, as long as the device output is still satisfactory.
- Required spare parts may be ordered just in time, saving storage space and minimizing down times.