Manufacturing
Quality AI for Auto Components
Customer Profile
A Tier 1 automotive component supplier with 4 manufacturing plants across Maharashtra and Gujarat, supplying to major OEMs including Tata Motors, Mahindra, and Maruti Suzuki. The company is a beneficiary of the PLI (Production Linked Incentive) scheme for automotive components.
Challenge
The company had deployed AI-powered visual inspection systems for quality control, but faced significant challenges that threatened both customer relationships and PLI compliance:
Inconsistent AI Performance
- Quality inspection AI showing 15% variance across shifts and plants
- Night shift rejection rates 20% higher than day shift
- Newer plant in Gujarat underperforming versus established Maharashtra facilities
- No explanation for why specific parts were rejected
Customer Audit Concerns
- OEM customers conducting rigorous AI governance audits
- Questions about AI decision-making they couldn’t answer
- Risk of losing preferred supplier status
- Increasing documentation requirements for AI-based inspection
False Reject Problem
- 8% false positive rate causing material waste
- Rejected parts requiring manual re-inspection
- Production line disruptions for false alarms
- Cost of quality higher than industry benchmarks
PLI Compliance Risk
- Scheme requires consistent quality metrics
- AI inconsistency creating documentation challenges
- Risk of incentive clawback for quality issues
- Need for auditable quality records
Solution
Rotavision deployed AI trust infrastructure across all 4 manufacturing plants:
Vishwas for Fairness & Consistency
- Bias detection across shifts, plants, and production lines
- Root cause analysis for performance variance
- Continuous calibration to ensure consistent thresholds
- Demographic parity across operating conditions
Guardian for Production Monitoring
- Real-time accuracy tracking for each inspection station
- Drift detection for model degradation
- Automated alerts when performance deviates
- Historical trend analysis for predictive maintenance
Explainability Layer
- Visual heatmaps showing defect detection reasoning
- Confidence scores for each inspection decision
- Audit-ready documentation for every rejection
- OEM-compatible reporting formats
Calibration Framework
- Standardized lighting and camera setup validation
- Cross-plant model synchronization
- Shift-agnostic performance targets
- Seasonal variation compensation
Implementation
| Phase | Scope | Duration |
|---|---|---|
| Assessment | Baseline variance analysis across plants | Week 1-2 |
| Root Cause | Identification of bias sources | Week 3-4 |
| Calibration | Model and environment standardization | Week 5-8 |
| Deployment | Guardian monitoring across all stations | Week 9-12 |
| Audit Prep | OEM documentation and training | Week 13-14 |
Root Causes Identified
The variance analysis revealed several bias sources:
| Issue | Impact | Resolution |
|---|---|---|
| Lighting variation | 8% accuracy difference | Standardized lighting rigs |
| Camera calibration drift | 5% shift variance | Weekly auto-calibration |
| Training data imbalance | Plant-specific bias | Cross-plant data augmentation |
| Threshold inconsistency | False reject variance | Unified threshold framework |
Results
| Metric | Before | After | Change |
|---|---|---|---|
| Cross-Plant Variance | 15% | 2% | -87% |
| Shift Variance | 20% | 3% | -85% |
| False Reject Rate | 8% | 6% | -25% |
| OEM Audit Findings | 3 major | 0 | -100% |
| Inspection Documentation | Manual | Automated | 100% |
Customer Audit Success
The next OEM audit resulted in:
- Zero findings related to AI-based inspection
- Commendation for explainability and documentation
- Preferred supplier status retained
- Benchmark sharing request from OEM for their other suppliers
PLI Compliance
The improved quality consistency supported PLI scheme requirements:
- Consistent quality metrics across reporting periods
- Auditable inspection records for government review
- Reduced rejections supporting production targets
- Documentation ready for incentive claims
Financial Impact
| Category | Annual Savings |
|---|---|
| Reduced False Rejects | ₹1.2 Cr |
| Manual Re-inspection | ₹45 L |
| Audit Preparation | ₹20 L |
| Customer Penalty Avoidance | ₹80 L |
| Total | ₹2.65 Cr |
Plant Manager Feedback
“Our OEM customers were asking questions about our AI that we couldn’t answer. Now we have complete visibility into why the AI makes each decision. The last audit was the smoothest we’ve ever had.” — Plant Head, Pune Facility
What’s Next
The company is expanding AI trust infrastructure to:
- Predictive maintenance for CNC machines
- Supply chain demand forecasting
- Energy consumption optimization
- Worker safety monitoring
Rotavision is powered by Rotascale’s globally-proven AI trust platform.
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