Watch the full interview: http://www.isixsigma.com/tools-templates/design-of-experiments-doe/mark-kiemele-interview/
Michael: Somebody may be watching this interview right now, Mark, and say, “Well, I have worked in health care and I know that AIDS is a statistical issue, and one hundred and thirty factors. I just work in human resources, or I just work in marketing at my company. Like you are talking about stuff that is rocket science. I am just a human resource manager.” Can you give me an example from a functional area at a Fortune 100 company, such as human resources or marketing, where design of experiments can be applied?
Mark: Absolutely. The one that comes to mind, Mike, comes from Boston Fleet Bank, which is now part of Bank of America. This was done, I think, in 2004, maybe eight or nine years ago. Done by a young lady and her team in the HR department at Boston Fleet Bank. Their problem was turnover. Turnover was the response. That is the variable that was creating problems. When you have high turnover rates that is expensive. It costs money to bring people in — to hire people and to train them up to speed. And worse than that was sometimes an unmeasurable things like the high turnover rate was in areas where these folks were interfacing with the customers. Okay? And that is tough stuff.
Mark: I know DOE, but I am not an HR person, but these guys know HR. So, these guys said, “What are the factors that could be contributing to these high turnover rates?” Now, I probably would not have come up with stuff like this, but time since last promotion. Educational history. I might have gone to the educational history thing. Job stability history. What is the local unemployment rate at the time somebody left? What is the local employment alternative? What is the company’s market share? Then you have got the company’s policies, like what is the lateral upward mobility climate like? The layoff climate. There are all kinds of factors. All of those things are factors. Well, guess what? They investigated 16 or 17 factors, and they narrowed them down to two or three that were really critical that allowed them to change their policies on supervision. Supervisor stability. That is not their mental stability. That is how long they were engraved. That turned out to be a very, very important factor. So, it changed their policy that supervisors would stay in their positions longer. They would have more training for their supervisors. And one of the other factors that was important- statistically significant anyway — was how they recruited these people. Did they get them through an agency or were they hired based on internal recommendations? And the internal recommendations folks tended to stay longer. So those factors started coming out. And the beauty of the model that they developed was they got data. Every time there was somebody who would leave the company they got data, so they knew what the factor values were at the time somebody left, and they could affiliate that with that particular individual, and they rolled that back into their model. Continuously updating their model so they could predict and find what the factors, if there was any change in the factors, that are really affecting the output.
Mark: So, actually that example, Mike, was so impressive that it was written up in Harvard Business Review. So people can go to HBR and they can read about that particular DOE.