Dr.Ron's Insights: Statistics Roundtable-Raise Your Batting Average(1)

Design of experiments (DoE) has been an effective tool for experimenters, statisticians and quality professionals for decades. DoE has evolved since the
seminal work of Sir Ronald A. Fisher in the 1920s, and since George E.P. Box and his colleagues enhanced and popularized the approach in the process industries in the 1950s and 1960s. The utility of the method has even spread to the service industries, backed by a growing amount of literature.1, 2
A key aspect of experimentation is that it is sequential. Box emphasized that experimentation and learning is an iterative process, as shown in Figure 1. Problems are rarely solved and significant advances in knowledge are rarely made after a single experiment. Learning is a process, not an event. With some exceptions, a series of experiments is usually the norm.

When you look at the plethora of DoEbooks on the market, however, you see little discussion on the sequential (iterative) nature of the endeavor. This is due, in large part, to the statistics profession’s focus on individual statistical tools without thinking about how the tools are sequenced and linked to solve problems.

Some approaches

When sequential experimentation is discussed, it is addressed in a number of ways, all of which are effective under the right circumstances. A classic treatment of the subject is optimization of the product or process design via “hill climbing,”using the method of steepest ascent. A response surface method3 or a simplex optimization4 is used to guide the sequence of experiments.

Another approach is to run a fractionalfactorial design, perhaps followed by additional experiments to sort out any interactions identified by the fractionalfactorial design.

You can also use adesign that permits the estimation of linear effects or linear and interaction effects, and includes counterpoints to detect response-surface curvature if it exists. If curvature is detected, additional experiments are run using designs that involve three, four and five levels to estimate quadratic effects enabling the identification of optimal conditions.

We also often find situations in which, as the experimenter moves from one experiment to the next, the factor ranges may change (expand, narrow or shift), the center of the experimental region may be changed or variables may be added to or deleted from the study.

Each of these approaches is useful, but they can be made even more effective when included in an overall strategy of experimentation.

Keep reading:

Dr.Ron's Insights: Statistics Roundtable-Raise Your Batting Average(2)

Dr.Ron's Insights: Statistics Roundtable-Raise Your Batting Average(3)

About the author:
Ronald D. Snee, PhD is founder and president of Snee Associates, a firm dedicated to the successful implementation of process and
organizational improvement initiatives. He provides guidance to senior executives in their pursuit of improved business performance
using QbD, Lean Six Sigma, and other improvement approaches. Ron received his BA from Washington and Jefferson College and MS
and PhD degrees from Rutgers University. He is a frequent speaker and has published four books and more than 200 papers.