Before developing and running a full-blown experiment, a screening experiment is run to discover active factors so statisticians know what to include in the real experiment. A supersaturated design, "in which the number of factors exceeds the number of runs," is used when a large experiment is impractical. However, there is not enough information about the comparison and evaluation of various methods for supersaturated designs. This paper utilizes simulations using different sample sizes and number of active factors.
In the paper, it was stated that the "most flexible design construction methods are algorithmic." In other words, simulations studies reign supreme in supersaturated designs. Another helpful aspect of this article was the list of analysis of data, how the simulations were run to obtain the appropriate results.
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