Kyndryl automates defect detection on manufacturing lines with edge AI
Kyndryl deployed a custom defect detection solution in manufacturing environments to improve safety, reduce downtime, and streamline quality assurance. Using the Azure Percept edge AI development kit, Kyndryl built object detection models to monitor components on manufacturing lines in real-time. Images from production lines are processed at the edge, with results sent to Azure IoT Hub and analyzed with Azure Stream Analytics and Azure SQL Database. Visual insights are provided to plant managers via Power BI dashboards. This architecture reduced the need for manual inspections, accelerated response to quality issues, and enabled proactive maintenance. By integrating Azure Percept Studio for model training and deployment, the system quickly adapted to new product types, improving flexibility and operational efficiency.
- Organization
- Kyndryl
- Industry
- Manufacturing
- Location
- India
- Published
- March 2022
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 2 of 2
- 1Automated Manufacturing Defect Detection with Edge AI
- 2Real-time Component Counting and Quality Analytics
- Manual defect detection was labor-intensive and error-prone.
- Downtime and slow response to maintenance incidents increased operational costs.
- Limited real-time monitoring decreased worker safety and responsiveness.
- Implemented Azure Percept edge AI kit for on-site image capture and edge processing.
- Trained custom computer vision object detection models in Azure Percept Studio.
- Used Azure IoT Hub for collecting and relaying telemetry data to the cloud.
- Analyzed data flows with Azure Stream Analytics and stored results in Azure SQL Database.
- Enabled near-real-time data visualization using Power BI dashboards.
- Significantly reduced manual inspection time and associated costs.
- Improved real-time uptime and work safety.
- Enabled proactive quality management and adaptive processes.
Architecture
The solution uses Azure Percept DK at the edge for vision-based defect detection, aggregating component counts via a custom module. Telemetry is relayed to Azure IoT Hub, transformed with Azure Stream Analytics, and stored in Azure SQL Database. Power BI visualizes results for plant managers and operators. All model training and deployment use Azure Percept Studio, and system operation is monitored and tuned via Azure IoT Explorer.
Sources & evidence1
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2025.
Measures whether this deployment's public evidence persists — not whether the system is still in production.
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