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Use Case

Predictive Maintenance:
Before It Breaks

Arrêts non planifiés costs fabricants an average of hundreds of thousands per hour. Reactive maintenance catches failures after they happen. Scheduled maintenance wastes resources replacing parts that still have life in them.

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Equipment Health MonitorLIVEPress #194%HealthyPress #287%HealthyOven B362%WarningPress #431%CriticalPump #1278%MonitorFailure Prediction — Press #4Predicted failure in 4.2 days — Bearing wear pattern detectedThresholdNOWPredicted failure
La solution

How Jemba solves it

Jemba’s anomaly detection algorithm continuously monitors your equipment data, learning normal operating patterns and flagging deviations before they cause shutdowns — giving your maintenance teams time to intervene.

  • ✓Continuous real-time monitoring with <2s temps de réponse
  • ✓Learns normal operating patterns from your specific equipment
  • ✓Flags anomalies and early-warning signals automatically
  • ✓Dashboards that maintenance teams can act on immediately
−35%
arrêts de production (aggregate across deployments)
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Related use cases

Quality Correlation →OEE Optimisation →

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