Manufacturing · 8 months
Predictive maintenance ML prevents $4.2M in downtime
Industrial IoT data and a production-grade ML pipeline turned reactive maintenance into a profit center.
Headline result
$4.2M
downtime prevented in year one
The challenge
A precision manufacturer with 14 plants relied on time-based maintenance for high-value CNC equipment. Unplanned downtime averaged 3.2% of production hours, costing roughly $4.7M annually. Sensor data existed but sat unused in plant historians.
Our approach
- 01Unified data platform on Snowflake ingesting OT data from all 14 plants
- 02Feature engineering and model training for failure prediction across three equipment classes
- 03Production ML pipeline with monitoring, retraining, and human-in-the-loop review
- 04Operator-facing dashboards integrated with the existing CMMS for actionable alerts
- 05MLOps practice handed over to the internal data team with full documentation
Results
Measurable outcomes.
$4.2M
downtime prevented in year one
94%
model precision on critical alerts
18 days
median lead time on equipment failure alerts
1.7%
unplanned downtime (down from 3.2%)
Technologies
“We thought ML was years away for us. They had a working model in production in three months.”
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