A heatmap is a diagram for the two-dimensional color visualization of data. This visualization is used to provide an intuitive and quick overview of a large amount of data and to make particularly striking values easily recognizable. That is the formal definition. With the last major front-end update, heatmaps are now available to all oee.ai users.
Who doesn’t want that: See at a glance where it’s stuck. With heatmaps this is now easily possible at oee.ai. Heatmaps are available in the configuration as a widget so that this additional visualization option is accessible to all users.
Reading a heat map widget takes some practice. A period is always plotted on both the horizontal and vertical axes. This can be days, shifts or hours, for example. In this way, a grid is created in the widget area, the fields of which are filled with different color intensities. The darker the color in the quadrant, the more often or longer an event has occurred in the time interval. When interpreting, it must be ensured that, depending on the configuration, “lighter” or “darker” must be translated as better or worse.
Two examples: If a heat map widget has been configured for the output per hour or the MTBF, a darker color is better than a light one. However, if unwanted events have been configured in the widget, as in the example below, lighter colors are better than darker ones.
In order to always ensure the correct interpretation of the color fields, there is automatically a legend below each widget, which is defined by a color gradient. There you can read precisely what light and dark mean and where the respective scale ends.
If you look at the heat maps shown in image 1 with this prior knowledge, you can quickly see that there is a tendency for fewer downtimes to occur in the late shift than in the remaining shifts. Furthermore it can be seen that August 31st was a day with many microstops.
The heatmap widgets can be positioned anywhere in cockpits, reports or Andon boards.
On the above report you can see at a glance in the heat map at the bottom right that a high number of pieces per hour was produced on the night of the first reporting day, which was not reached again during the rest of the day. Active production then ended with the end of the late shift. On the lower day, the equipment was idle for most of the day and began to produce again at a moderate pace during the night shift.
If you have any questions about the possible uses of widgets in oee.ai, please do not hesitate to contact us.