Automotive Supplier Deploys Predictive Quality Control
93% defect escape reduction with 6-month ROI and $5.6M annual benefit
A Tier-1 automotive supplier producing precision components for major OEMs faced increasing quality demands. Their existing quality control relied on statistical sampling—inspecting only 5% of parts manually—which missed defects that reached customers. With a 0.3% defect escape rate triggering costly recalls and OEM customers threatening volume shifts, they needed a solution that could inspect 100% of parts without reducing throughput. Cloud-based computer vision solutions were rejected due to concerns about proprietary manufacturing data leaving the facility and latency requirements for in-line inspection.
Manual quality inspection created multiple bottlenecks. Human inspectors caught only 85% of defects through statistical sampling, and the 0.3% defect escape rate was triggering costly recalls and damaging OEM relationships. Production lines slowed during shift changes and breaks when inspection coverage dropped. Their major OEM customers were threatening to shift volume to competitors with better quality metrics. The technical requirements were demanding: inspection needed to happen in-line at production speed (<100ms latency), the system needed to operate 24/7 with minimal downtime, and integration with existing MES systems was mandatory. Cloud solutions couldn't meet the latency requirements, and proprietary manufacturing data—including OEM specifications and defect patterns—couldn't leave the facility.
- Defect escape rate of 0.3% triggering costly recalls and OEM penalties
- Manual inspection catching only 85% of defects through 5% statistical sampling
- Production slowdowns during inspector shift changes reducing throughput
- OEM customers threatening volume shifts due to quality metrics
- Cloud-based vision systems too slow for <100ms production line requirements
- Proprietary manufacturing data and OEM specifications couldn't leave facility
- Need to inspect 100% of parts without reducing production throughput
SLYD deployed a comprehensive edge AI system with separate training and inference infrastructure. For edge inference, 2 ruggedized GPU servers per production line (24 total across 12 lines) were deployed with NVIDIA L40S GPUs in factory-hardened enclosures. The system integrates with existing high-speed industrial cameras and PLC systems for reject triggering via simple air jet mechanisms. For training infrastructure, an on-premises cluster with 8× H100 GPUs enables continuous model improvement through an A/B testing framework with human-in-the-loop labeling for edge cases. Custom computer vision models trained on 6+ months of historical defect data process images at 45ms average latency. The deployment followed a phased approach: 8-week pilot on highest-volume line, 4-week validation period, then 12-week expansion phases to remaining lines.
- 24× NVIDIA L40S edge servers in ruggedized enclosures
- 8× H100 GPU training cluster (Dell PowerEdge)
- 12× high-speed industrial cameras per line
- OPC-UA integration for PLC communication
- PyTorch with TorchServe inference runtime
- Custom CNN architecture for defect classification
- Automated model update pipeline with A/B testing
- Centralized management from corporate data center
The deployment transformed quality metrics across the board. Defect escape rate dropped from 0.3% to 0.02%—a 93% improvement. False positive rate stayed at just 0.1%, minimizing good parts rejected. The system now inspects 100% of parts versus the previous 5% sampling. Financially, the impact was substantial: $3.2M in recall cost avoidance, $1.8M in warranty cost reduction, and $0.6M in inspection labor reallocation for a total annual benefit of $5.6M. System uptime reached 99.8%, exceeding targets. Inference latency averaged 45ms, well under the 100ms requirement. Model accuracy achieved 99.4% precision and 99.1% recall. The primary OEM customer increased volume allocation by 15%, validating the investment. ROI was achieved in just 6 months.
— VP of OperationsOur OEM partners now see us as an innovation leader rather than a quality risk. The AI system paid for itself before we finished the second production quarter, and we've since won three new contracts based on our quality capabilities.
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