Estimate the probable time for the 'next failure' of production-critical assets using a combination of physics and statistics based models.
Automatically generate predictive maintenance actions, in response to asset performance, to minimize or eliminate unplanned downtime using an intelligent built-in domain-specific rules engine.
Monitor Key Performance Metrics, and use machine learning to ensure continuous optimization of asset behaviour.
Track reliability of production-critical assets, and auto generate preventive maintenance based on usage rather than time.
Automated integration of production equipment with enterprise asset management systems for generating proactive maintenance work orders - removes the need for manually entering data in these systems.
Reduce failure rates via predictive maintenance, and facilitate real-time collaboration between maintenance personnel and domain experts.