In the early stages of studying a process, you identify a list of factors that potentially affect your response or responses. You are interested in identifying the active factors, that is, the factors that actually do affect your response or responses. A screening design helps you determine which factors are likely to be active. Once the active factors are identified, you can construct more sophisticated designs, such as response surface designs, to model interactions and curvature.

The Custom Design platform constructs screening designs using either the D-optimality or Bayesian D-optimality criterion. The D-optimality criterion minimizes the determinant of the covariance matrix of the model coefficient estimates. It follows that D-optimality focuses on precise estimates of the effects. For details, see Optimality Criteria in Custom Designs.