Extending the desirability function to account for variability measures in univariate and multivariate response experiments

One technique used frequently among quality practitioners seeking solutions to multi-response optimization problems is the desirability function approach. The technique involves modeling each characteristic using response surface designs and then transforming the characteristics into a single performance measure. The traditional procedure, however, calls for estimating only the mean response; the variability among the characteristics is not considered. Furthermore, the approach typically relies on the accuracy of second-order polynomials in its estimation, which are not always suitable. This paper, in contrast, proposes a methodology that utilizes higher-order estimation techniques and incorporates the concepts of robust design to account for process variability. Several examples are provided to illustrate the effectiveness of the proposed methodology.

► The desirability function approach is a multi-response optimization technique.
► The traditional method involves seeking the optimal mean response.
► We extend the approach to include variance and covariance measures.
► Higher-order estimation techniques are also incorporated to enhance precision.
► Examples illustrate how it can be extended to univariate optimization problems.