A blocked staggered-level design for an experiment with two hard-to-change factors

Staggered-level designs have been introduced in the literature as cost-efficient and statistically efficient alternatives to split-plot and split-split-plot designs for experiments with multiple hard-to-change factors. In this article, we present an application of a staggered-level design to a staple fiber cutting process at Eastman. The experiment was run in blocks and involved one quantitative hard-to-change factor, one two-level categorical hard-to-change factor, and three quantitative easy-to-change factors. We review existing work on staggered-level designs, discuss D-, A- and I-optimal staggered-level designs and blocked staggered-level designs, and perform an analysis of the data from the staple fiber cutting experiment.