Batch Reporting

introduction in these how to guides, we will describe how to build three grafana dashboards for monitoring and analysing batch production operations these are docid\ tczshwhbcgwzhm5gvsbk9 a real time dashboard for floor operators and shift leads, showing the current status of a mixer, live process signals and recent batch step history docid eay8wetswftziqbzv8ki an analytical dashboard for process engineers, allowing side by side comparison of process signals, energy consumption and step durations across multiple batches docid\ hrheyoxam8lm6vkh8rxdj a weekly summary dashboard for shift leads and production managers, showing batch throughput, energy consumption and quality results across both production lines dashboard overview operator dashboard batch comparison week report audience floor operators, shift leads process engineers shift leads, managers time scope real time, last 6 hours per batch (event scoped) week by week complexity beginner intermediate intermediate–advanced operator dashboard the operator dashboard is a good starting point it introduces two fundamental query types asset measurement queries for live process signals, and event queries for batch step history it covers the most common panel types stat , time series , state timeline and table batch comparison dashboard the batch comparison dashboard builds on the operator dashboard concepts and introduces more advanced query patterns and data transformations the central technique is using periodic event queries with the fetch asset properties option, enabling meaningful shape comparison across batches regardless of when they ran week report the week report is the most advanced of the three dashboards it introduces panel repeat and a non trivial transformation pipeline for the daily throughput charts the quality pass rate panels use a multi step calculate field pattern to compute a ratio from grouped event counts the weekly time picker quick ranges can only be configured by editing the dashboard json directly, which is also covered prerequisites make sure you have the following prepared an instance of factry historian if you do not have an instance of factry historian at your disposal, you can run it locally using our docid\ myrfsdthxlh5xqk06ik m data from faketory loaded as measurements consult the docid\ myrfsdthxlh5xqk06ik m or the docid\ tjw2vwpk12gu3z5awhr4a pages for more information a configured faketory docid\ in ezegwrotydez3cgrye and associated docid 9v8e0c mkyeqdc85wpd9h installed and configured docid 9enc3pgv14ed p2oatbtj we recommend loading all data for the faketory in a separate organization in factry historian and configuring the datasource plugin against this organization