Case study · Manufacturing
IoT Analytics for Manufacturing
Streaming IoT ingestion, anomaly detection, and predictive-maintenance ML.
Key results
- Unplanned downtime -23%
- Maintenance cost -18%
- OEE +7 points
Context
An industrial manufacturer had IoT sensors on most production equipment but the data flowed into siloed historian systems rather than the analytics platform. Predictive maintenance was a goal the team had tried to implement three times before without producing production-deployable models.
Challenge
Previous attempts had failed on data quality and the gap between prototype ML and production operational patterns. The engagement had to solve both simultaneously.
Approach
Thoughtwave delivered an 11-month IoT analytics program: streaming ingestion from historians into a lakehouse, anomaly-detection models trained on historical equipment-failure data, maintenance-team workflow integration so model outputs reached the floor in production, and continuous-evaluation pipelines for model drift. The engagement covered discovery, data platform build, ML engineering, and full operational handoff.
Outcomes
Unplanned downtime dropped 23% across the production lines in scope; maintenance cost dropped 18% through predictive-maintenance replacing time-based maintenance; OEE improved 7 points — a material metric for the business.
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