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:
- Operator dashboard 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.
- Batch comparison dashboard An analytical dashboard for process engineers, allowing side-by-side comparison of process signals, energy consumption and step durations across multiple batches.
- Weekly report dashboard 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 Docker setup guideDocker setup guide.
- Data from Faketory loaded as measurements. Consult the Docker setup guideDocker setup guide or the FaketoryFaketory pages for more information.
- A configured Faketory Asset treeAsset tree and associated EventsEvents.
- Installed and configured Factry Historian Datasource plugin for GrafanaFactry Historian Datasource plugin for Grafana.
We recommend loading all data for the Faketory in a separate organization in Factry Historian and configuring the datasource plugin against this organization.