Visualizing Schedule “Tradesies”
We recently met with an operations executive that was confronting one of the more vexing issues a manager faces. An employee told her there is a rumor going around that employees are swapping shifts with each other to increase their pay. In this organization, anyone who is not scheduled to work but then works is entitled to premium pay.
One of the well known ways to exploit this type of policy (I explained this as “tradesies” in my book Lean Labor) is to find a couple of buddies and regularly swap shifts with each other. It may not be every shift, but you have to trust your partner enough to know that you can be up or down a shift and when the time comes, they will agree to swap out a shift with you.
This of course drives up costs without increasing output. Not a good outcome for any organization. The executive was pondering what to do. The challenge is that there are many legitimate reasons to swap shifts and the policy is intended to provide flexibility for the workforce but ensure coverage for the workload. A premium may be paid to encourage employees to work hours that they might not otherwise want, thereby providing liquidity to the system.
Addressing this would be tough because it’s difficult to discriminate between legitimate shift swaps and ones that were done purely to increase pay. But the rumor was expanding and if this practice spread it could ultimately lead to lower profits, poor morale and even layoffs of uninvolved people.
Before she acted, she asked my team to take a look at her organization’s scheduling data and see what we could find. This is a fairly challenging exercise because first you have to figure out who actually swapped shifts with who. There is no “marker” other than a premium paycode for one person. After that was resolved, we had a long list of shift swaps. Next we had to figure out a way to visualize that list to help interpret the data.
After a couple of different approaches, the team was excited as they realized this would call for a different type of visual approach. The reason they were excited is that the vast majority of visualizations required are bar, line and scatter charts. These charts do a great job, but we all like some variety!
In this case the team realized they were looking at a networking relationship between the people swapping shifts.
Using a networking diagram, they plotted the employees and who they swapped a shift with. What we wanted to know though was not only who, but how many times shifts were swapped since gaming typically occurs between a small group of people. For that we colored the arrow differently based on the number of swaps made over the time period analyzed.
Below is the result of the effort. As you can see, the majority of the swaps are occasional and with a variety of people. Good news! Most people are swapping shifts as the organization intended. But after applying a filter to remove the occasional swaps there are two clusters of three people that are swapping significantly more times and with the same people. This doesn’t necessarily mean they are gaming the system. It’s possible that they have very specific skills and there is a limited pool of people they can swap with.
This information was illuminating for the executive. Out of thousands of people, she could now focus on six and get to the bottom of it quickly. She could also respond to the rumor with hard facts. Finally, it was peace of mind for her to know that the vast majority of her employees were using the policy as it was intended.