Faster Process Improvements
Increase in Output While Maintaining Quality
Improvements in First Pass Yield
key quality metrics to maintain process control.
quality failures in advance and take preventative actions fast.
material waste with live scrap prediction rates.
extended unplanned downtime with predictive maintenance.
operations in real-time with predictive alerts and alarms.
Predictive analytics models are then deployed in the cloud or on the edge which limits connectivity dependency and lowers latency for business-critical applications. Machine learning tools run models against live production data to predict quality failures allowing factory personnel to take corrective action faster. Predictive analytics tools also look for a specific set of conditions that indicate a quality or machine failure may happen. For example, if temperature and vibration measurements on a machine reach certain levels for a specific amount of time there is an increased likelihood the machine will fail. Models are continuously retrained in the cloud and then automatically updated on the edge when there are significant improvements or changes.