Experimental Design (DOE, Industrial Experimental Design)

In industrial settings, Experimental design (DOE) techniques apply analysis of variance principles to product development. The primary goal is usually to extract the maximum amount of unbiased information regarding the factors affecting a production process from as few (costly) observations as possible. In industrial settings, complex interactions among many factors that influence a product are often regarded as a "nuisance" (they are often of no interest; they only complicate the process of identifying important factors, and in experiments with many factors it would not be possible or practical to identify them anyway). Hence, if you review standard texts on experimentation in industry (Box, Hunter, and Hunter, 1978; Box and Draper, 1987; Mason, Gunst, and Hess, 1989; Taguchi, 1987) you will find that they will primarily discuss designs with many factors (e.g., 16 or 32) in which interaction effects cannot be evaluated, and the primary focus of the discussion is how to derive unbiased main effect (and, perhaps, two-way interaction) estimates with a minimum number of observations.

For more information, see the Experimental Design Introductory Overview.