Watch the full interview: http://www.isixsigma.com/tools-templates/design-of-experiments-doe/mark-kiemele-interview/
Michael: What is design of experiments?
Mark: Mike, let me give you my elevator speech for DOE, or design of experiments. DOE is the best data collection strategy that is out there today when our goal is to investigate relationships between inputs and outputs of a process. Now, let me explain inputs and outputs of a process. A process that most of us are very familiar with, Mike, is driving an automobile, or owning an automobile. And one of the performance measures, or responses, that we might be interested is gas mileage — miles per gallon. That is how we typically measure gas mileage. Now, there are other measures of performance or responses, like: “How long does is take to go from zero to sixty miles per hour?” But let me just talk about miles per gallon a second. That would be considered an output of the process — of this automobile process, if we want to call it. It is a performance measure. Sometimes we call it a response variable. It is also an output. Now, we can surmise what various inputs might be to that, that affect that output. Well, one might be, and our experience level with an automobile is going to tell us that, and one might be tire pressure. Maybe tire pressure would be the factor. Now, if we tested it at two different levels, like 25 psi versus 35 psi, tire pressure would be the factor, and the two levels at which we test it would be, say, 25 and 35 psi. Another factor that we could surmise might affect gas mileage is the type of fuel that we are using. Is it 85 octane? 91 octane? So, fuel type might be another factor. The two settings, or the two levels, that we might want to test fuel type at are maybe — I know, in Colorado, we have 85 octane. You may not have that in Washington, but we may go to a low setting, like 85 octane and a high setting at 91. So that gives the followers here a little bit of an idea of what a factor is or an input. Sometimes we call these factors inputs and outputs. But by and large, DOE is the best way to collect data when you want to find relationships between inputs and outputs, Mike.
Michael: Okay, that makes sense. And if I was an automobile manufacturer and I realized how important that miles-per-gallon rating was of the cars I was manufacturing with gas prices nearing five dollars a gallon here, in Washington State, I know that that is likely one of the top factors influencing whether somebody buys their car or buys some other car, so I need to optimize my miles per gallon on that car and I want to produce the best miles per gallon possible. So, what are the factors that go in there? Clearly I need to have something that is energy efficient, but there are a lot of factors that go in. Besides just the engine, it is the tire pressure, and it is exactly what you said earlier. And so that is what DOE allows you to do. Optimize an output variable based on a bunch of different input factors.