Automotive · 12 weeks · A leading automotive OEM
Predictive analytics for production quality
Client name anonymized under NDA. Industry, technical approach, tools, and measured outcomes are reported as-is. Named references available on request.
# RESULTS
- Fewer false positives
- 40%
- Faster triage
- 2.5×
- End to end
- 12 wk
- New tools to learn
- Zero
# THE-CHALLENGE
False-positive overload in defect detection. The existing quality system flagged everything — real defects, sensor noise, calibration drift. Production line managers spent more time triaging alerts than fixing actual problems.
Every shift started with hundreds of alerts, most of them noise. The team had learned to ignore the system entirely — which meant real defects slipped through at the same rate as before the system existed. A quality tool nobody trusts is worse than no tool at all.
# THE-TRANSFORMATION
// before
- Quality system flagged everything — real defects, sensor noise, calibration drift
- Hundreds of alerts per shift, most of them false positives
- Operators learned to ignore the system entirely
- Real defects slipped through at the same rate as before
// after
- 40% fewer false positives — only real anomalies trigger alerts
- 2.5× faster triage with probability-scored alerts
- Dashboard adoption 90%+ within the first month
- Data quality issues caught at ingestion, not by end users
# OUR-APPROACH
Data audit
Mapped every sensor feed, quality checkpoint, and historical defect record. Found three data sources nobody knew existed — including a calibration log that explained 60% of the false positives.
Model development
Built classification models in Python/scikit-learn trained on actual defect outcomes, not just threshold breaches. Tuned for precision over recall — fewer alerts, higher confidence.
Dashboard integration
Connected predictions to a Power BI layer plant managers already used. No new tool to learn. Alerts now show probability scores, not just pass/fail.
Validation & handover
Ran the model alongside the old system for 4 weeks. Documented every discrepancy. Trained the quality team to retrain the model when product specs change.
The turning point was discovering a sensor calibration log buried in a shared drive that nobody had linked to the quality data. It explained why one production line had 3× the alert rate of others — the sensors were miscalibrated after a maintenance cycle, and the quality system had been faithfully flagging the drift as defects for four months.
# TECH-STACK
Working with Juri and Oleks, the project was surprisingly uncomplicated. What mattered most to us was being able to trust the alerts again — and that is exactly what showed up in daily operations. We argue about the data far less now and deal with the actual problems instead.
# OPERATING-CONTEXT
Operating constraint
No new tools allowed. The quality team had already lost trust in one system and wouldn't adopt another. We had to ship predictions into the existing Power BI layer they already used for shift reporting — no separate dashboard, no new login.
Adoption & rollout
4-week parallel run against the legacy alert system before cutover. Every discrepancy between the two was documented and reviewed with the quality team. Dashboard adoption crossed 90% within the first month post-cutover. Quality team trained to retrain the model themselves when product specs change.
Common questions about this project
How does predictive quality work in automotive manufacturing?
A model learns which combinations of sensor readings and process parameters historically precede real defects, and flags those patterns before they become scrap. The hard part is not the model — it is calibrating against false positives so the quality team trusts the alerts. In this project that meant 40% fewer false alarms and predictions delivered inside the Power BI layer the team already used, not a new tool.What data does a predictive quality system need?
Sensor and process data from the line, quality outcomes to label against, and — often forgotten — maintenance and calibration logs. Here, a buried calibration log explained a 3× alert rate on one line; without it the model would have learned the miscalibration as a defect pattern. A data audit before modelling is where these gaps surface.How do you introduce predictions without disrupting production?
Run the new system in parallel with the existing alerting until the numbers demonstrably agree — here a 4-week parallel run, with every discrepancy documented and reviewed with the quality team. Cutover happens when trust exists, not when the model is ready. Adoption crossed 90% within the first month.