I am a relative newcomer to the use of DOE in R&D: I'd been a long-time skeptic but have recently come to see the HUGE power that it offers, and have embraced JMP as my tool of choice. In the process of learning how to apply DOE methodologies, I've come to think of its preferred implementation as a sort of "bottoms-up" approach: we start by identifying ALL the factors that might contribute to a measured outcome, and then create a design that allows us to screen the impact of each factor, identifying the most critical ones. We can generate a model to represent the system of interest, dropping from that model factors that do not contribute significantly, then refining our model with those drivers. It seems to me that we might occasionally find that our model STILL does not represent our observations well - and perhaps we've overlooked a factor or two. We can remedy this by augmenting our model. For a complex system with a bundle of factors, this conventional approach makes for a big test matrix. I know I can reduce the size of that matrix by making use of fractional factorials and custom / optimal designs. For a skeptical client AND a complex system, this still makes for a tough sell. But: is there something necessarily BAD about starting with a "top-down" approach? Could we use our subject matter knowledge to identify some handful of factors that we believe to be the performance drivers, and with them a test matrix and then a statistical model of observed data? If this leads to a good fit of observed data, then GREAT. But if this model falls short in adequately addressing the observations, then we could use this evident shortcoming in the model to improve it by incorporation of additional factors (some of that big bundle that we'd chosen to set aside at the outset …) and more testing? Would not the mathematics of "top-down" and "bottoms-up" approaches tend to converge on the same model? Or is my notion of a "top-down" approach really just a variation on a custom design?
