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Industrial Manufacturer: Predictive Maintenance

IoT streaming from 14 plants into Databricks, predictive maintenance models, OEE +6.4 points, unplanned downtime −41%.

The challenge

An industrial manufacturer was losing €24 M annually to unplanned downtime across 14 plants. Sensor data sat in plant historians with no central aggregation, and maintenance was calendar-based rather than condition-based.

What we delivered

  • 01
    Azure IoT Hub ingestion of 48,000 sensors across 14 plants.
  • 02
    Databricks Delta Live Tables streaming pipelines with bronze / silver / gold.
  • 03
    Predictive maintenance ML models with MLflow tracking, champion-challenger, and drift monitoring.
  • 04
    Maintenance scheduling integration with SAP PM.
  • 05
    OEE dashboards on Power BI with plant-level drill-down.

Outcomes

  • −41% Unplanned downtime.
  • +6.4 pts Overall equipment effectiveness (OEE).
  • €24 M Annual unplanned downtime cost avoided.
  • 48,000 Sensors streaming with <5 s latency.
“Condition-based maintenance is now our default, not an aspiration.”

— VP Operations, Industrial Manufacturer