Watch the full interview: http://www.isixsigma.com/tools-templates/design-of-experiments-doe/mark-kiemele-interview/
Michael: Can you give me a DOE example from government use?
Mark: Well, I could give you hundreds of those, Mike, that involves ships. Large systems like ship, subs, aircraft, ground vehicles, and also systems that are now being designed to be prevent successful cyber attacks. I could give you those, but the one I want to give you is one that I think we can all relate to, and that is AIDS. The spread of AIDS has been a big problem and the State Department, years ago, asked us at the Air Force Academy to investigate the key factors that influenced many, many response variables, one of which is the propagation of AIDS. And so it is a big problem. Lots of factors that are involved. And they had a model that was built by scientists at Los Alamos National Labs and also the Miriam Research Center at University of Illinois. And they had like 360 differential equations. They were deterministic differential equations that had (Unclear 21:08), and somehow we had to make sense of all of that. So, we got it down to one hundred and thirty-four factors. About one hundred and thirty factors that we wanted to investigate. Well, what kind of design do you have for evaluating one hundred and thirty-four factors simultaneously? Well, today it would be easy, because we have the software — the hardware to do this. But back in those days, Mike, we did not have that, so we had to generate a one hundred and thirty-six design, which is called a Plackett-Burman design. It does not matter what you call you it, but it is one hundred and thirty-six test cases or runs, as we would call them, and we did that and we were able to flesh out the most important factors. That was a screening design, where you screen out or separate out the vital few from the trivial many. So that was one of the largest designed experiments I was involved in some years ago. Now, with design of new automobiles and things like that, you are dealing with lots of factors like that again. Simulators have lots of factors. And that was essentially what this was. It was a simulator, but they were differential equations. Very complex stuff, and you had to fair it out — the most important factors. And it was interesting that the State Department folks that heard the last briefing and got the reports that it is really interesting now that we can prioritize these factors and we can now start looking at what we have to act on. Where do we spend the money now to, in fact, reduce this propagation of AIDS?
Michael: Got you. So that sounds like a great example. It is something that is very complex from a socio-economic perspective. One hundred and thirty factors. That is the kind of thing that would boggle my mind to try and solve. How do you solve AIDS? But you used a bunch of experts, you narrowed it down to one hundred and thirty factors, you then put it into a design of experiments using a specific design that you mentioned, and you screened it to find which factors were actually important to the output and which factors actually were not that important. Maybe there was some personal bias from a PhD who is an expert in some area of age or society, and you were actually able to use the data to then find the truths. And what happened from that study, Mark? Is there something that was measurable that the government was able to use in order to affect the spread of AIDS?
Mark: Absolutely. I cannot remember the top seven, but once we got the top seven or so, we were able to then build modeling designs, Mike, that would allow us to get at the interaction effects, because that is where the keys are. They keys are in those doggone combination effects or interaction effects that you have. And by golly, we could then, in fact, find some interaction effects. And that led the government to say, “Oh, well, this factor by itself is not as important as the other factor, but when you combine them together, their combined effect is much greater than each one individually.” So that allows them to home in on the factors, and then, of course, like you said, the socio-economic impact is huge and you have got to zero in on what can you do from a socio-economic point of view to minimize the impact of those factors. That is the real hard part. The DOE, Mike, is not hard. That is the point. The point is, is we think the easy stuff is hard and the hard stuff is easy. The hard stuff is once you know the factors; now what are you going to do about it? Where are you going to put your money? How are you going to impact those factors?