Sustain a high quality production
Through machine learning, you can find patterns in the historical data that led to improved or decreased quality, thus giving possibility to know what to do to avoid quality issues or increase quality from satisfying to even better.
A well-trained machine learning model can recognize, even beforehand in some cases, situations where quality is likely to get off-track and actions that were taken to return to desired quality. Once deployed in real-time execution, AI solutions can anticipate quality issue and prescribe process changes to avoid or minimize quality issue.
Trained in a more efficiency-driven approach, a machine learning model can also detect the optimal settings of production process that that resulted in top-tier product quality output. In production, such a model can make the process converge and maintain to these optimal conditions.
With a better control of process quality, the risks of reprocess/scrapping are drastically reduced and associated with savings in process costs with better quality products and associated price to the market.