Identify optimization potential at a glance with the heat map

A heat map 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 distinctive values easy to recognize. That is the formal definition. With the latest major front-end update, heat maps are now available to all oee.ai users.

Who wouldn't want that: See where it's stuck at a glance. With heat maps, this is now easily possible with oee.ai. Heatmaps are available as widgets in the configuration, so that this additional visualization option is accessible to all users.

Reading a heatmap widget requires practice. A period of time is always plotted on both the horizontal and vertical axes. These can be days, shifts or hours, for example. In this way, a grid is created in the widget area whose fields are filled with different color intensities. The darker the color in the quadrant, the more frequently or longer an event has occurred in the time interval. When interpreting, make sure that, depending on the configuration, “lighter” or “darker” must be translated as better or worse.

Two examples: If a heatmap widget has been configured to run per hour or MTBF, a darker color is better than a light one. However, if adverse events have been configured in the widget, as in the example below, lighter colors are better than darker ones.

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 means and also where the respective scale ends.

If you look at the heat maps shown in Figure 1 with this prior knowledge, you can quickly see that there tend to be fewer downtimes in the late shift than in the remaining shifts. It can also be seen that 31.08.21 was a day with many microstops.

The heat map widgets can be positioned anywhere, in cockpits, reports, or Andon boards.

In the report above, the heat map at the bottom right shows at a glance that a high number of units were produced per hour on the night of the first reporting day, which was not achieved again during the rest of the day. Active production then ended with the end of the late shift. On the lower day, the plant stood still for the longest time of the day and began producing again at a moderate pace on the night shift.

If you have any questions about the uses of widgets in oee.ai, feel free to contact us.