Original article was published by Frederik Bussler on Artificial Intelligence on Medium
19 are responsible for the highest number of hours lost, referring to musculoskeletal diseases and general injuries, respectively.
Musculoskeletal diseases include tendinitis, carpal tunnel, bone fractures, and so on. The dataset is sourced from a courier company, which means that this data reflects relatively hard labor, compared to a typical desk job. Research shows that musculoskeletal diseases are indeed quite common in the courier industry, as repetitive, heavy lifting takes its toll on the body.
Reducing Absentee Hours
Since most absentee hours are lost due to these injuries, courier companies could effectively reduce absentee hours by implementing a more ergonomic workplace, training proper lifting and posture, and reducing overtime — all things that would reduce injuries.
Looking at the original list of key factors, we can see that the second-most important attribute is
work load average/day, providing evidence to the hypothesis that overworking leads to greater injury, and thus absenteeism hours.
One research paper found that “high mental stress at work” was an important risk factor for work-related injury in the courier industry, which means that companies could also reduce absentee hours by implementing workplace wellness schemes and better supporting stressed employees.
How Any Company Can Reduce Absentee Hours
Perhaps you’re in a completely different industry, in which case, your employees’ absentee hours may have completely different causes.
Fortunately, the power of predictive insights is that it’s widely applicable. You can find predictive insights in any dataset that has a KPI column and attribute columns, so you can run the same analysis in any industry. If you don’t have a dataset that looks like the one we’ve analyzed, you can simply make one.