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Réparez-le avant qu'il ne se brise. Pas après.

L'IA qui surveille l'état des équipements, détecte les signaux de panne et recommande des actions de maintenance. Ainsi, les temps d'arrêt imprévus deviennent une exception gérée et non une crise récurrente.

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The problem

Unplanned downtime derails operations without warning

  • Unplanned downtime that derails operations

    Equipment failures happen without warning. The cost isn't just repair — it's lost production, missed SLAs and emergency contractor rates.

  • Scheduled maintenance that wastes resource

    Fixed-interval maintenance replaces parts that are still functional and misses components that are actually degrading.

  • Sensor data collected but never acted on

    Equipment generates enormous volumes of operational data. Without AI to interpret it, that data sits unused while failures develop.

  • Plans disconnected from operations

    Maintenance schedules ignore production priorities and asset criticality. Downtime shifts rather than shrinks.

Fonctionnement

From sensor noise to work orders—with fewer false alarms

Étape 1

Ingest machine data

Vibration, temperature, throughput, and maintenance history are aligned to asset hierarchy.

Étape 2

Forecast failure windows

Models estimate risk horizons and recommended actions—parts, crew, and downtime trade-offs included.

Étape 3

Close with CMMS

Recommended work orders are created or updated; feedback loops improve predictions per asset class.

Edge deployments supported when plants cannot rely on constant cloud connectivity.

Réparez-le avant qu'il ne se brise. Pas après.

Ce qui est inclus

What you get when you run this with Thinkia

A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.

Sensor data integration

Connects to IoT sensors, SCADA systems and operational data sources across your asset base.

Failure prediction models

Detects early signs of component degradation before failure occurs.

Maintenance recommendation engine

Suggests the right maintenance action, at the right time, for the right asset.

Asset health dashboard

Real-time view of equipment status, risk scores and upcoming maintenance priorities.

Work order automation

Triggers maintenance work orders in your CMMS when thresholds are crossed.

Failure pattern library

Builds an organisation-specific library of failure signatures that improves prediction accuracy over time.

Propulsé par Thinkia Synapse

Résultats

What changes when this runs in production

Results vary by asset type, sensor coverage and operational environment.

–40%

Reduction in unplanned equipment failures after deployment

–25%

Reduction from eliminating unnecessary scheduled interventions

+30%

Improvement in asset reliability with condition-based maintenance

Notre façon de travailler

From calendar maintenance to signals that prevent unplanned stops

Asset criticality

Week 1–2

Lines, sensors, and failure modes are ranked by downtime cost and data availability.

Model & alert

Week 3–5

Thresholds, lead times, and work-order integration are tuned with maintenance planners.

Field pilot

Week 6–9

Technicians validate alerts; false positives are reduced before broadening asset classes.

Fleet scale

Week 10+

Spares, crews, and schedules align to predicted demand; continuous learning respects safety gates.

Sensor coverage and industrial network policies set pace; we start where telemetry is trustworthy.

Commencer

Ready to scope this for your context?

We start with a focused session—no commitment—to map constraints and a sensible path.

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