Picture the hero of our story. A tough, rugged, shiny new Enterprise Mobile Device. She’s reliable, she’s helpful, and she works tirelessly throughout the day to herald top-line growth and increase employee efficiency. She is truly a hero, but is she enough? How many heroes does it take to save the day?
(Superhero metaphor over…)
- After a Mobile POS App rolled out, was there a resulting increase in basket size? Was there a decrease in abandoned carts?
- After a Receiving App was released, did the average time to receive a truck decrease? Did overtime hours decrease?
What is more difficult to know about a mobile device investment is:
- Did I purchase the right number of mobile devices to enable my employees?
- Could get a good return if I purchased more?
- Are my devices correctly distributed across my sites?
These questions *used to be very difficult to answer. Until… Support Analytics. Support Analytics is a platform that tracks mobile device usage (and 60 other device metrics), and provides companies with the mobile device answers they are looking for.
We used Support Analytics to track how employees at Company 1 used devices over 2 months.
Now let’s explore what Company 1 learned about the sizing of their mobile device fleet.
Here is our plan for observing the fleet:
- Establish how many devices and users are at each site.
- Observe when and how many devices are used throughout the day.
- Understand if more (or fewer) devices are needed for the system.
How Many Devices and Users are at Each Site?
Company 1 has five sites. Each site has between 17-23 devices, and while each has a different manager, the processes are largely the same.
Now while the number of devices at each site are similar, the number of distinct users to touch a device over the course of a week is much different, with up to 130 different people using devices at Site 1, while only 70 use devices at Site 5.
To give a better picture of users and devices, we can look at the user-to-device ratio. In the graph below, we see this average ratio calculated on a day-of-the-week basis (device usage tends to have a weekly cyclical pattern). What we see is that Site 1 has the most users per device, while Site 2 (not Site 5) has the fewest.
When are Devices Used During the Day?
While we know that Site 1 has the highest ratio of users per device on a daily basis, we can’t make any conclusions until we know what that looks like during the day. Below is a graph that shows the average number of devices in use throughout the day.
What we see here is interesting. Sites 1 and 4 have distinctly different usage patterns than Sites 2, 3 and 5. 1 and 4 have much more even usage throughout the day and night periods, while 2, 3 and 5 have a large drop-off between day usage and night usage. But would 1 and 4 have similar patterns if they had more devices to use during their peak hours?
One way to dig into this deeper is to look at the days with peak usage: Tuesday and Thursday.
This view paints a more extreme picture of what we were seeing before. Sites 1 and 4 utilize devices much more during the entire day than Sites 2, 3 and 5. And for Site 1, there appears to be a difference of only 3 devices in use, on average, between the night and the day.
To end our investigation of device use over time, we will take a look at the standard deviation for usage. We will use Thursday for our example.
As we see below, all sites have a standard deviation of between 1-2 per devices in use per hour, except for Site 2. In part, the higher standard deviation of Site 2 could be attributed to our small sample size (8 Thursdays), but we would also argue that the large standard deviation shows us that employees have enough devices to respond to fluctuations in their work environment. Ie, the other sites would also have higher standard deviations if they had more devices.
How to Right-Size this Fleet
Before we make a device recommendation for Company 1, there are a couple of areas we would want to investigate first.
- How are devices allocated to employees?
- What activities (by whom and when) are the main drivers for top-line or bottom-line growth for this company?
- How does device cost relate to revenue and savings resulting from device use?
- Are there any processes that are different between the 5 sites?
But, for the sake of this exercise, and given that the above questions relate to sensitive information, here are our conclusions!
Sites that would use more devices (in order of need): Site 1, Site 4, Site 3, Site 5
Sites where we would not increase devices: Site 2
During peak hours, 4 of the 5 sites would likely use more devices. The operative question is, if employees were given more devices, how much time would it take for their resulting activities to pay for the investment?
Now we are aware that we just concluded a statistical analysis by saying that a site needs ‘more devices.’ How many devices is that actually?
To provide that specific conclusion, we would need to have some on-site observation, to address what the optimum user-to-device ratio is, as well as what peak usage looks like in a healthy environment.