The goal of a robust parameter design is usually to determine factor settings that will minimize the variability of the response about some ideal target value (or target function in the case of a dynamic response experiment). Taguchi methods do this by a two-step optimization process. The first step concentrates on minimizing variability, and the second focuses on hitting the target.
- First, set all factors that have a substantial effect on the signal-to-noise ratio at the level where the signal-to-noise is maximized.
- Then, adjust the level of one or more factors that substantially affect the mean (or slope) but not the signal-to-noise to put the response on target.
An alternative approach is to start by minimizing the standard deviation and then adjust a factor that affects the mean but does not affect the standard deviation.
