Theory of Screening Procedures to Identify Robust Product Designs Using Fractional Factorial Experiments

In many quality improvement experiments, there are one or more “control factors” that can be used to determine a final product design (or manufacturing process), and one or more “environmental factors” that vary under field (or manufacturing) conditions. This paper considers applications where the product design or process design is considered to be seriously flawed if its performance is inferior at any level of the environmental factor; for example, if a particular prosthetic heart valve design has poor fluid flow characteristics at certain pulse rates, then a manufacturer will not want to put this design into production. In such a situation, the appropriate measure of a product’s quality is its worst performance over the levels of the environmental factor. This paper develops theory for a class of subset selection procedures that identify product designs which maximize the worst-case performance over the environmental conditions for general Combined array experiments.