Client Overview
A leading electronics component manufacturer based in Southeast Asia, known for its high-volume production of microchips and PCB assemblies. The company sought to minimize human error in quality checks and improve traceability without slowing down the assembly line.
Our Approach
Steps included:
- Identifying critical inspection points across the assembly line
- Setting up multi-angle industrial cameras for micro-inspection
- Training AI models to detect over 50 defect types
- Integrating barcode and RFID scanning for traceability
Challenge
Manual quality control processes were causing:
Machine Vision
India
Key pain points included:
- Inconsistent defect detection
- Delays in production due to visual inspection bottlenecks
- Missed micro-level defects, leading to increased returns
- Limited traceability of rejected batches
- High operational cost for quality assurance
Solutions Delivered
AI-Powered Vision Inspection System
Achieved >98% defect detection using multi-angle imaging and deep learning at high speed.
Defect Dashboard with Visual Heatmaps
Displayed categorized defects and severity levels with real-time visual overlays and alerts.
RFID & QR Code Traceability System
Enabled full batch tracking and rejection mapping across all production checkpoints.
MES & QMS System Integration
Connected vision data with MES and QMS for automated logging and quality traceability.
Technologies Used
Business Impact / Results
The deployment of machine vision drastically improved quality outcomes:
What Our Clients Say
"QeMatic's RPA team transformed our manual loan processing system. What once took days now happens in hours. Their deep platform knowledge and process-driven approach helped us reduce turnaround time by over 60% while staying fully compliant."
“We engaged QeMatic to integrate AI with our existing automation efforts. The results were outstanding — predictive quality control and demand forecasting brought a new level of intelligence to our operations. They truly understand the future of automation."
"QeMatic's Time and Motion Study uncovered hidden inefficiencies in our patient onboarding flow. Their insights led to a 30% improvement in staff productivity and better patient experience. The free consultation alone was eye-opening."
Key Highlights (Summary Box)
| Metric | Before Implementation | After Implementation |
|---|---|---|
| Inspection Accuracy | ~85% (Manual) | 99.1% (Automated Vision) |
| QA Resource Dependency | 12 Inspectors per shift | 4 per shift |
| Defective Units Dispatched | 1.8% | 0.3% |
| Batch Traceability | Partial | End-to-End with RFID |
| Detection Time per Unit | 6–7 seconds | 2 seconds |
