Case study · Operations
Predicted on the line.
Not found at the end.
A German supplier of parts to premium carmakers found defects at final inspection and fixed machines after they stopped. We turned their line data into warnings that come before the problem.
Before
Machinessensor data+Inspectionquality results+Repairsmaintenance logs=No warningthree systems that never talked
- Defects found after the parts were made.
- Machines fixed after they stopped.
- Scrap and downtime counted at month end.
We audited it, then split it three ways.
- Mechanical work
One stream of line data
Machine, inspection and maintenance data flow into one place, continuously, matched to the same part and the same minute.
- Policy
Control limits
Statistical limits flag drift the moment a process starts to wander. No AI needed, and every alert says what moved.
- Judgement
Prediction
Models learn the patterns that come before a defect or a breakdown, and warn ahead of time, so the line is adjusted or serviced first.
What changed
- −35%
- parts waste
- −67%
- machine downtime
- Maintenance planned before breakdowns, not after them.
- Quality managed as it happens, not counted at month end.
Stop piloting.Start compounding.
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