Predictive Quality
Predictive quality uses process and sensor data to forecast the quality of output before or as it is produced, so operators can correct drift early and reduce scrap, rework and off-spec product — rather than discovering defects only at final inspection.
By modelling the relationship between process conditions and product quality, predictive quality flags when a batch or run is trending out of spec while there is still time to act. It reduces waste, improves first-pass yield and complements automated inspection. It relies on good historical data linking process settings to outcomes, and is a common early win for industrial machine learning.
Related terms
Machine Learning (Industrial) · Soft Sensor · AI Vision Inspection (Machine Vision QC) · SPC (Statistical Process Control)
Related guides
Software
Seeq
Advanced analytics for time-series process data.
Cognite Data Fusion
Industrial DataOps and digital-twin foundation.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.
Where this applies
Implementing a line-clearance procedure · State of AI in Food & Beverage Manufacturing 2026 · State of AI in Pharmaceutical Manufacturing 2026