- April 18, 2019
April 18, 2019 –TrendMiner NV, a Software AG company and provider of the leading self-service advanced analytics software, has announced the release of their second software update of 2019, TrendMiner 2019.R2. This release introduces DashHub, a dashboard and reporting module that provides actionable, visual representation of operational performance data. In addition to DashHub, the 2019.R2 release also includes features and enhancements based on user feedback.
TrendMiner works to enable process and asset experts to analyze, monitor and predict operational performance through trend analysis of time-series data. Multiple trend views, in combination with specific views of contextual operational performance data, can now be presented via the actionable dashboard, DashHub. With DashHub, process behaviors related to specific KPIs can be monitored, and trends of key sensor data can be viewed, enabling detection of root causes that can lead to actionable intelligence when issues or losses occur.
The user can switch between available dashboards or quickly create new dashboards with trend or context item tiles. In addition to the notifications users can receive based on monitors they have created, dashboards can be used to assess operational performance and prioritize which parts of their process require special attention. From DashHub, users can directly investigate process anomalies, production losses or equipment inefficiencies.
The recently introduced ContextHub brought users the ability to get a full 360 view of their manufacturing processes by unlocking all operational events stored in third party systems. Users can jump between the graphical views of TrendHub and the contextual views of ContextHub. Context items can be visualized directly within their respective trend views, and conversely, context items in ContextHub can be used to access their respective trend views with a push of a button.
Also new in 2019.R2 is the ability for context item information such as quality results, batch numbers, or results from 3rd party systems to be added through flexible field creation. This additional information can be used to filter data in a way that suits the user, which in turn can help to speed up data analysis activities of subject matter experts.