Visualising Schedule ‘Tradesies’

person holding phone

Guest blog: Gregg Gordon, vice president, data science practice group at Kronos Incorporated

Gregg Gordon oversees the analytics, data science, and big data focused group at Kronos. Author of Lean Labor: A Survival Guide for Companies Facing Global Competition and Your Last Differentiator: Human Capital, Gordon led the manufacturing business practice at Kronos for eight years prior to his latest role. At present, Gordon is committed to providing superior value to Kronos customers through the newly formed team of data scientists, business intelligence designers, and strategic consultants. Combining research and development with consultancy, Gordon’s team provides data driven solutions for Human Capital Management technology, deployment, and change management support. 

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 rumour going around that employees are swapping shifts with each other to increase their pay. In this organisation, 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 organisation. 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 rumour 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 organisation’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 visualise that list to help interpret the data.

After a couple of different approaches, the team was excited as they realised this would call for a different type of visual approach. The reason they were excited is that the vast majority of visualisations required are bar, line and scatter charts. These charts do a great job, but we all like some variety!

In this case the team realised 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 coloured the arrow differently based on the number of swaps made over the time period analysed.

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 organisation 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 rumour 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.