Taguchi's robust-design technique, also known as parameter design, focuses on making product and process designs insensitive (i.e., robust) to hard-to-control variations. In some applications, however, his approach of modeling expected loss and the resulting “product array” experimental format leads to unnecessarily expensive experiments. As an alternative to Taguchi's “loss model/product array” formulation. Welch, Yu, Kang, and Sacks proposed combining control and noise factors in a single array, modeling the response itself rather than expected loss, and then approximating a prediction model for loss based on the fitted-response model. In this article, we further develop and strengthen this response-model/combined-array approach. We recommended examination of control-by-noise interaction plots suggested by the fitted-response model. These plots can reveal control-factor settings that dampen the effects of individual noise factors. We also show that the run savings from using combined arrays are due to the flexibility that this formulation allows for estimation of effects. In contrast, the product-array formulation dictates estimation of many effects that are unlikely to be important.