Dashboard: Utilization

Content:

Basic requirement for the dashboard

• Advanced analytics subscription (contact Sales if you wish a demo)

• Sensors for the workspaces need to be analysed (Room, Desk, Huddle)

Where does the data come from

The data for this Dashboard comes from the occupancy Sensors. We only analyse occupancy data on selected working days between the selected working hours (These can be changed in the building details). Data outside this period is currently not taken into account. We calculate the usage of a workspace per day, if the sensor has times without occupation which are less than 15', we calculate it as occupied for the whole period. (e.g. occupancy between 8:00 until 9:00 and from 9:10 to 10:00 the usage for this period is 2h / if the break is from 9:00 to 9:20 the usage would be 1h 40')

Categorization vs. Number of hours

As a workspace manager, you are looking for patterns, outliers and key differentiators between the workspaces. When comparing them, the hours of utilization may appear to be the right metric, but when averaged over several days, important differentiation can be lost.

Let’s take a look to an example:

Here, each desk has an average occupancy of 2 hours. But as you can see, there is a clear difference. While the desk 1 has been well used on Monday, it was not occupied during the rest of the week.

Based on several years of experience, ROOMZ has developed a machine learning-based algorithm, allowing the simple categorisation of occupancy and bookings. The workspace will show as one of the following; Very Rarely, Rarely, Frequently, or Very Frequently.

Note: unlike the basic analytics, working hours no-longer needs to be taken into consideration for this algorithm.

Organization view

If you have set your Filters, you will be able to see a report similar to the one below. Clicking on the various bars prompts the report to change, showing you only the selected data:

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1. A short overview of the workspaces in this report.

   1. In this report we see a lot of rarely used workspaces, this could be improved

2. In the weekly profile you are able to see how the different workspaces are used during the week. (The data for each day of the week is aggregated over the weeks or months selected,)

   1. In this example we see clearly that the office is not well-used towards the end of the week.

3. The monthly profile shows the occupancy over the months. (If you have selected more than one year, the data will be aggregated for the months, in the example above, we have two years' of data aggregated for Jan, Feb and March)

4. In the selected time period, you are able to see the data over a specifically selected time period.

   1. In the buildings, you can compare the selected buildings or floors by opening the data with the +.

5. This is an easy way to see how the spaces are used differently in various buildings, or on different floors.

6. If you have set tags , you will see data relating to them reported here too.

   1. This is a great way to analyse how different teams use their workspaces.

You are able to expand every part of the Report separately by opening the details with a right mouse click (select show as a table):

Open

You will now see a larger view with the exact numbers for each section:

Floor view

On the top bar, if you change the view to Floor, some details of the report will change: 

1. On most utilized you see the 3 most and the 3 least used workspaces.

2. If you have more than one floor in the selection, you are able to switch the floor using the navigation. Also, you can zoom in/out.

3. Using this view of the floorspace, you will easily see the workspaces and how they are used.