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Increase warehouse transparency with Power BI heatmaps — October 13, 2017

Increase warehouse transparency with Power BI heatmaps

You can increase transparency in your warehouse by applying heatmaps to visualize data from your warehouse. The heatmap can visualize data like pick frequency, fill percentage, ABC slotting, work exceptions and other warehouse data directly onto a warehouse floorplan. The visualization of data onto the physical layout of your warehouse, provides additional insight into warehouse data and transactions compared to traditional warehouse reports.

In a previous post, I discussed how you can create your own warehouse performance reports using Power BI and Dynamics 365 for finance and operations – enterprise edition (D365FOE). In this post I will explore this opportunity further by discussing how you can combine D365FOE warehouse data with the heatmap concept.


Creating a pick frequency heatmap in Power BI

As an example I will create a pick frequency heatmap, that will display the pick frequency as a color for each of the locations in the warehouse. The darker the color – the higher the pick frequency:

Pick frequency - close up.png

The heatmap is good at revealing anomalies and patterns that are difficult to extract from normal D365FOE screens / reports. An example of this is location D016 in the heatmap above. Why is this remote pick location so heavily trafficked? And conversely why is one the best picking locations D124 so rarely visited. The colors indicates that items on these locations should be re-slotted.

If your pickers experience congestion problems – you can also use the pick freqency heatmap to identify traffic jam areas in the warehouse. Once the traffic jam areas has been identified, you can reslot your items to avoid this.

In the next sections I will walk through the required steps to create the heatmap.

Import the Synoptic panel visualization

First step in creating the heatmap is to import the Synoptic Panel visualization into Power BI.  You can import it either from a file (downloaded from OKviz site) or from the store.



Synoptic panel2

Create heatmap template

Before you can create the heatmap in Power BI, you must first create a heatmap template on the webpage. Drag an image of your warehouse floorplan on to the empty canvas and start to map the locations:

Synoptic designer


When you have mapped all locations and given them the correct location names (it’s very important they are named precisely the same as your location names in D365FOE), then click Export to Power BI and save the presented image on you local machine:

Export to Power BI

Now you have a picture containing your warehouse floorplan including mapped locations.

Create heatmap in Power BI

Now you are ready to create your heatmap in Power BI. Start by clicking the Synoptic panel visualisation.

Select synoptic panel


Add the entity store as a datasource:



Select and load the WHSwarehouse_Worklines measure:


Now setup the Category and measure as displayed below. The field dragged into the Measure area will be the field visualized by color. I have selected Worklines, because that can be interpreted as the pick frequency.

Edit 10-26-2017: After posting the blog I found that it’s not correct to use the standard worklines measure as a count for worklines. It will not be correct if you have more than 1 pick line in your work. You should create a new measure instead counting the worklines:  worklinesnew = COUNTROWS(WHSWarehouse_WorkLines). You should use this instead in the examples below. This is what happens when ERP consultants starts playing with Power BI 🙂



Now setup the colors and their corresponding thresholds:



Now you are ready to import your heatmap template:

Import template

Now refresh your data and view the result of your work:


As mentioned above you can use the heatmap to display a variety of other warehouse data, including

  • location fill percentage
  • slotting of your ABC items
  • work exceptions
  • locations requiring replenishment
  • worker activity in the warehouse
  • Last cyclecount

In my next post I will explore some of these further. Until then you can watch this nice podcast with Fredrik Sætre, Mirza Abdic and others, where they also talk about Power BI heatmaps..