Business problem
Unplanned failures and weak prioritization consume maintenance capacity while the costliest equipment losses remain hidden.
Back to solutions
Availability
Predictive Maintenance
PrismaWerk combines sensor history, alarms, maintenance records, and process context to surface the equipment failures that cost the most capacity. The result is a maintenance workflow that moves teams from reactive firefighting to targeted prevention.
See how it works
Expected outcomes
- +Earlier warning on equipment degradation
- +Maintenance work ranked by throughput impact
- +Reduced Availability losses and clearer MTBF/MTTR conversations
Methods
Anomaly detectionFailure predictionMaintenance prioritizationCapacity-at-risk modeling
Workflow
From operating question to usable system.
- Step 1
Connect equipment, process, and maintenance data sources.
- Step 2
Model degradation patterns and rank interventions by expected production impact.
- Step 3
Deliver alerts, maintenance priorities, and validation metrics for daily reviews.
Related proof
Case studies connected to predictive maintenance.
AvailabilityManufacturing
Prioritized Maintenance Platform: $2.4M Annual Value
A maintenance prioritization system that used simulation to rank interventions by workshop-level capacity impact and quantify annual value.
- Annual Value
- $2.4M
- Payback Period
- 30-60d
- Recovered/72h
- 5.3h
AvailabilityManufacturing
Manufacturing Performance Monitoring Suite
A monitoring suite that consolidated sensor, alarm, and production data into dashboards for daily manufacturing performance reviews.
- Sensors Monitored
- 200+
- Auto-Updates
- Daily
- Visibility
- 100%
Next step
Ready to explore predictive maintenance?
Bring a current process, line, or data challenge. PrismaWerk will help identify the strongest first move.