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