Supersaturated designs: set-ups, data interpretation, and analytical applications

Abstract

For screening purposes, two-level screening designs, such as fractional factorial (FF) and Plackett-Burman (PB) designs, are usually applied. These designs enable examination of, at most, N-1 factors in N experiments. However, when many factors need to be examined, the number of experiments still becomes unfeasibly large. Occasionally, in order to reduce time and costs, a given number of factors can be examined in fewer experiments than with the above screening designs, by using supersaturated designs. These designs examine more than N (SS)-1 factors in N (SS) experiments. In this review, different set-ups to construct supersaturated designs are explained and discussed, followed by several possible data interpretations of supersaturated design results. Finally, some analytical applications of supersaturated designs are given.