Response Surface Methodology—Current Status and Future Directions

Abstract

This paper is a reflection on where response surface methodology (RSM) is at this point and what will likely be future directions. The emphasis in the last two decades on robust parameter design has brought attention to RSM as an alternative methodology for variance reduction and process improvement. While computer generated design technology has been beneficial to those who are interested in constructing RSM designs, changes are needed in this area to allow consideration of design robustness rather than design optimality. RSM is moving into areas involving the use of generalized linear models (GLM's), and optimal RS designs for these areas are either difficult or impossible to implement by the user. Example applications of GLM's include logistic and Poisson regression. Other RSM areas that will enjoy use by practitioners in the twenty-first century include multiple responses and nonparametric and semiparametric methods. In addition, design and analysis techniques for cases where natural restrictions in randomization occur need to be addressed further and communicated to users.