The folks over at Minitab recently wrote an excellent article about how a Six Sigma practitioner leveraged binary logistic regression analysis in order to better understand why associates at a manufacturing plant were quitting.
What they learned
The team discovered that the distance the employee drove to work seemed to impact how likely they were to quit.
Put another way, there seemed to be strong correlation between the distance of the employees commute and how likely they were to quit.
Sure, people can nitpick sample size and whether there truly is causation… but, really, the analysis seems sound.
Revamp the interview process
As a result of what they learned this team decided a potential countermeasure was to revamp the interview process. Specifically:
The manufacturer’s HR department used the results of Parks’ analysis to revamp the interview process for manufacturing positions. They began to look more closely at the potential employee’s commuting distance and took this into account before making hiring decisions.
Are they missing the mark?
When I first read this I couldn’t help but ponder whether this company was really addressing the root cause of the problem.
Sure, driving 45 minutes to work may stink and could lead to folks wanting to find another job.
But is the best answer to simply not hire them? Should the HR team seek some additional countermeasures?
Obviously, an assembler or machine operator can’t really work from home… but could their hours be shifted to avoid traffic? Perhaps there are other things at work here in addition to the long commute?
What do you think?
This is not an easy situation to assess and I’m sure this team put a lot of thought into this particular problem… but is it possible they’re missing the mark with their “don’t hire based on commute length” countermeasure?
What do you think?