Quality should not depend on manual checks or on detecting errors when the product has already moved down the line.
Industry Automotive components manufacturer
-50% errors and repetition of processes
+40% Product quality
↑ productivity Time reduction and production efficiency improvement.
↓ incidents Fewer defective parts moving down the line
↓ demand problems Under- and oversupply is avoided.
BEFORE
Quality review depended on manual inspections and different criteria among operators.
Defects were detected late, when the part had already advanced in the process.
It was difficult to know where the error occurred and which lot was affected.
NOW
Cameras automatically inspect every part on the production line
The system detects defects, classifies incidents and issues real-time alerts.
Each control is recorded to know what part happened, when, where and with what result.
THE PROBLEM
Defects were detected too late
Slow and difficult to scale manual inspections.
Dependence on the criteria of each operator to validate parts.
Errors detected when the part had already advanced in production.
Lack of traceability to know where and when the incident occurred.
THE SOLUTION
Computer vision for parts inspection
Automatic image capture at different points along the line.
Deep learning models trained to detect visual defects.
Real-time alerts when a part does not meet quality standards.
Logging and traceability of each inspection to analyze incidents and patterns.
THE IMPACT
More quality without slowing down production
Errors are detected when they occur, not when they have already generated a major problem. Quality ceased to depend on manual reviews and became a continuous and automatic control.
