


• Historical data collection from production systems.
• Using AI to analyze and select relevant information.

• Developing AI models based on historical data.
• Building models to predict quality test results.

• Applying predictive model to evaluate real-time data as products pass through specific points in the production line.
• Prediction of probabilities of passing or failing quality tests.

• Automated decision on the need for manual quality testing based on Quality Prediction.
• Redirecting resources to higher risk areas and optimizing the inspection workflow.

Production
Optimization

Reduction in
Production Time

Increase in Production
Capacity

Flexibility and Efficiency
in Working Between
Machines and Humans

Maximizing
Employee Time

Minimizing Error
and Human Burden

Quality and Consistency
Improvement

Quick and Assertive
Decision Making

Predict irregularities/defects at any point of the production line.

Predict the probability of failure in critical components of assembly line machines, allowing proactive maintenance and reducing downtime.

Assess the conformity of final finishing and assembly processes.