Experimental Design - Summary

Experimental methods are finding increasing use in manufacturing to optimize the production process. Specifically, the goal of these methods is to identify the optimum settings for the different factors that affect the production process. In the discussion so far, the major classes of designs that are typically used in industrial experimentation have been introduced: 2^(k-p) (two-level, multi-factor) designs, 2-level screening (Plackett-Burman) designs for large numbers of factors, 3^(k-p) (three-level, multi-factor) designs (mixed designs with 2- and 3-level factors are also supported), Central composite, non-factorial, surface designs, Latin squares, Greco-Latin squares designs, Taguchi robust design experiments (orthogonal arrays) analysis, Mixture designs and triangular surfaces, and special procedures for constructing experiments in constrained experimental regions. Interestingly, many of these experimental techniques have "made their way" from the production plant into management, and successful implementations have been reported in profit planning in business, cash-flow optimization in banking, etc. (e.g., see Yokoyama and Taguchi, 1975).