| Features |
Description |
|
|
Customer Value |
| Real-time data |
Continuous machine sensor data ingestion |
|
|
Performance visibility. Predictive interventions. Rework cost reduction. |
| Fault Detection |
Aggregates and analyzes multi-asset data for early fault-detection and mitigation |
|
|
Optimizes machine ecosystem reliability. Ability to identify and eliminate recurring faults to improve machine and factory efficiencies. |
| Duty Rates |
Active measurement of machine uptime via continuous monitoring of sensors and controls |
|
|
Optimizes machine productivity and promotes more accurate production planning. |
| Heath Scores |
Quantified health scores based on multivariate analysis of real-time data |
|
|
Live insights into machine health and productivity. Improves machine and factory OEE. |
| Predictive Maintenance |
Predictive modeling for forecasting accurate maintenance intervals |
|
|
Reduction in machine downtimes. Reduced maintenance cost. Higher predictability. |
| Root Cause Analysis |
Multidimensional analysis identifies fault drivers and correlating impactors |
|
|
Faster and more accurate fault identification. Elimination of repeat failure mechanisms. |
| Intelligent Workflows |
AI-enabled, automated, workflows that learn, adapt, and evolve. |
|
|
Intelligent automation of critical processes and notification routines. Reduced reliance on human oversight. |
| Gen AI |
Transformer-based models generate rapid insights based on historical and real-time data |
|
|
Faster and more impactful insights. More accurate extraction of information from existing knowledge base. |
| Centralized Visualization |
Customizable dashboarding of data and KPI from all connected assets. |
|
|
Provides an holistic view of data from the entire machine ecosystem, Can be scaled locally and globally. |
