The problem
Unplanned downtime is the most expensive hour in manufacturing, and exhaustive quality inspection is the second — sampling misses defects, full inspection doesn’t scale. Both problems have published, quantified AI solutions at industrial scale.
The system
Vibration/temperature/process telemetry feeding anomaly and failure-prediction models with lead time measured in days, not minutes; and process-data quality models that predict which units actually need physical inspection, so people inspect the flagged few instead of sampling blindly. Both backtested on your historical failures and defects before anyone trusts them.
How it's built
- Sensor/telemetry ingestion with data-quality gates (bad sensors lie confidently)
- Failure-prediction models validated against your maintenance logs
- Quality models on process data — inspection effort routed to predicted anomalies
- Alert design ops teams accept: lead time, confidence, and recommended action
Delivery
Sprint backtests on your maintenance and defect history; Build instruments the highest-cost line first.
What to expect
- Failure lead-time and precision measured against your own history
- Inspection effort concentrated where the model flags, not spread thin
- Capacity recovered from breakdowns that never happened
Documented results in the wild
Independent, published deployments of this class of system — cited as market evidence that it works at scale. These are not our clients.
- PepsiCo (Frito-Lay) ML machine-health monitoring added 4,000 hours/year of manufacturing capacity across four plants. WSJ CIO Journal, 2022 ↗
- Audi ML weld-quality analysis covers ~1.5M spot welds per shift vs ~5,000 manually sampled before — staff now check only flagged anomalies. Audi MediaCenter, 2023 ↗
- Google DeepMind models cut data-center cooling energy by up to 40%. Google DeepMind, 2016 ↗