Analyzing Fatigue in the Public Sector Workforce
Every government decision falls under public scrutiny, and that’s why public sector organizations must maximize the services delivered by their workforces. Of course, they must do this while making sure they’re remaining compliant and that their staff isn’t getting burnt-out in the process. What are the risks of employee burnout and fatigue?
- 95 percent of human resource leaders admit employee burnout is sabotaging workforce retention (The Workforce Institute at Kronos Employee Survey)
- Worker fatigue increases the risk for illnesses and injuries
- Fatigue can cause lack of motivation and impaired decision making
Organizations have a wide base of information to guide cost-effective overtime and scheduling decisions that can benefit their staff by helping avoid fatigue and burnout. Workforce management solutions allow agencies to analyze the data behind overtime and understand who needs a break.
Our data-science practice team at Kronos has continually helped organizations analyze their workforce and discovered stories that surprised the folks they’ve helped! Below is a Q&A with Michael Tice, a strategic consultant on the data-science practice who helps organizations solve their workforce management issues.
Q: Mike, can you walk us through how you analyze fatigue in a public sector organization?
A: Sure, I’m happy to. Let’s take a look at the example below. This is a real local government with anonymized data samples, and as such, the detailed data has been deliberately left as unidentifiable.
This graph is a straightforward way to analyze fatigue in the public sector. On the y-axis, we have employee IDs with the highest number of consecutive days worked for that employee on the x-axis. Consecutive days include weekends, so this means the top employee in the graph worked 41 days in a row during the time span of the analysis without a single day off. That’s over a month! This type of analysis can help us visualize employees who are working long stretches of consecutive days, enabling us to quickly help lead to more proactive solutions on giving these employees time off to break up these stretches.
Q: That was probably an eye-opener to that organization. What else did you help show them?
A: We recognized that the graph above didn’t tell the whole story. If the top employee who worked 41 days in a row also worked other stretches of consecutive days, those other stretches would be left out because this graph tells us only the highest number of consecutive days for each employee. We used the graph below to help shine a light on the full picture.
Each bubble represents an individual employee. On the vertical axis we are measuring the average number of hours worked in fatigued weeks by each employee and on the horizontal axis we have the number of fatigued weeks worked by each employee. While it fluctuates for different sectors, we define a fatigued week in public sector as a week in which an employee works more than or equal to 60 hours. For example, the right-most bubble tells us this employee worked 12 weeks over 60 hours, with an average of 67 hours worked in those 12 weeks.
Thanks, Mike. That’s a powerful example and something that organizations would act on very quickly if they had the tools to see what was going on.
Workforce data is an organization’s biggest asset, after their workforce of course. Using a workforce management solution with embedded analytics allows organizations to have a pulse on what their employees are doing and monitor people who might need a break. Fast, easy access to this kind of information leads to better engagement for the employee and helps organizations avoid messy compliance issues while also enabling them to drive down costs.