Predictive quality analytics helps in extracting actionable insights from data sources such as industrial equipment, environment, and human observations. Predictive quality analytics uses statistical algorithms such as machine learning to predict future outcomes by determining patterns and trends. The objective of predictive quality analytics is to determine needs such as using different sources of raw materials, adjusting machine settings, or training the workers that will improve the quality of the factory output.
By using Industrial IoT software and services, industrial manufacturers can establish predictive quality models, which are build upon the data, ingested from all the devices on their factory floors such as conveyors, robots, rotors, and valves. As a result, one can spot quality issues before they cascade down the production processes. The consistently higher quality products will make your customer delighted and will reduce product recalls.