In his text, Understanding Design of Experiments, R. J. Del Vecchio wrote, “Eventually, I did acquire reasonable competence in the understanding of basic Design of Experiments methodology. But the learning had proved to be difficult, not only because of my less-than- outstanding math skills, but also because of the difficulties resulting from varied and sometimes confusing nomenclature, contrasting approaches taken by different experts, and the tendency for many Statistics authors to write in a manner more suited to other statisticians than to run-of-the- mill scientists, engineers and industrial workers…”
Jim Leonard’s introduction to DOE mirrored Del Vecchio’s frustrating experience. He started to wonder, “Why does it have to be so difficult?” This workshop introduces a methodology for designing and analyzing experiments that is much less complicated than ANOVA and other traditional approaches. It presents DOE as a logical extension of intermediate and SPC tools, rather than a highly complex technique that’s piled on top of past and current practices.
We will start with a review of systems thinking and where DOE applies in both R&D and upgrading existing manufacturing processes. Then we’ll examine a simplified, step-by-step sequence of techniques for analyzing factor effects on both performance and consistency – without need of another license for a statistical software package. The session will close with an overview of a procedure that attendees can use to plan and manage their future experiments. At the end of this presentation you will have a renewed appreciation for DOE as a strategic initiative to help organizations remain competitive and profitable in this new economic age. You will also appreciate why a working knowledge of DOE is turning out to be a critical skill set for all technical professionals and managers. But it doesn’t have to be so complicated!