Production Line Scenario Control Using Part-Level Quality and Carbon Data

The patent merges three separate analytical approaches (quality estimation, carbon footprint estimation, and cost estimation) into a single integrated system that processes live data at part level. The supervision device combines multiple prediction algorithms that simultaneously consider quality parameters, carbon footprint, and cost, using real-time data from the production line to generate accurate predictions for each part individually.

The system implements feedback mechanisms by continuously receiving live data from the production line and using prediction algorithms to generate real-time estimates of quality, carbon footprint, and cost. These predictions are fed back to the supervision device, which can then adjust manufacturing parameters or alert operators to potential issues, enabling continuous improvement and accurate tracking throughout the production process.

The patent segments the production process into individual part-level evaluations rather than treating batches as homogeneous units. Each part is assessed independently using live data collected during manufacturing, allowing for precise prediction of quality, carbon footprint, and cost for each specific part. This segmentation enables targeted decision-making and traceability along the production process.

The system enables self-service by automatically collecting live data from sensors and production line equipment, processing it through prediction algorithms, and generating real-time assessments without requiring manual intervention for each part. This automated approach maintains high processing speed while achieving part-level prediction accuracy.