Root cause analysis and diagnosis uncovers the early causes of process inefficiencies to reduce unplanned downtime, increase production throughput, and minimize quality and yield issues. Process flow and production batch data are fused with historical and real-time sensor data. Machine Learning tools then trace correlations between the consolidated data and the process disruption events. Quality and maintenance engineers use these automated lists of prioritized suggestions to quickly find and mitigate the root causes of process inefficiencies and machine failure.
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