- Predictive maintenance - Service equipment based on data specific to each component – not generic guidelines – for a higher level of precision and more efficient maintenance spend
- Automated continuous monitoring - Trade daily inspections for sensor-based reporting, allowing maintenance manager to more effectively deploy technicians, improving labor efficiency and reducing maintenance hours
- Machine learning - Simulate expected behavior of monitored assets with greater accuracy by the day, determining when deviations occur and enabling greater maintenance precision
- Integration - Reduce administrative and clerical work required to keep facilities running via integration with computerized maintenance management systems, ordering, inventory and work order delegation tools
Figure 1. Real data example of an impending failure detection by MHS Insights followed by corrective action and a return to standard levels.