Screening: This phase explores the effects of a large number of variables, with the objective of identifying a smaller number of variables to study further in characterization or optimization experiments.

Additional screening experiments involving additional factors may be needed when the results of the initial screening experiments are not promising. On several occasions, I’ve seen the screening experiment solve the problem.

When there is very little known about the system being studied, sometimes range-finding experiments are used, in which the candidate factors are varied one factor at a time to get an idea of what factor levels it would be appropriate to consider. Varying one factor at a time can be useful.

Characterization: In this phase, you experiment to better understand the system by estimating interactions and linear (main) effects.

Optimization: In this phase, using response surface contour plots and perhaps mathematical optimization, you develop a predictive model for the system that can be used to find useful operating conditions.

Keep in mind Dave Bacon’s observation— particularly when working with an existing process—that there may be only time, money and process availability to run a single experiment.5 This situation is covered by the strategy of planning ahead, considering all factors and performing multiple experiments when an SCO experiment is used to solve the problem. I have seen such a strategy work on a number of occasions. The SCO strategy in fact embodies several strategies, which are subsets of the overall SCO strategy:
? Screening-characterization-optimization.
? Screening-optimization.
? Characterization-optimization.
? Screening-characterization.
? Screening.
? Characterization.
? Optimization.

The end result of each of these sequences is a completed project. There is no guarantee of success in any instance, only that SCO strategy will raise your batting average in hitting on the right answers.

The strategy used depends on the experimental environment, which includes the objectives of the experimental program. Criteria that can be used to
characterize the experimental environment are outlined in Table 2.

These characteristics involve program objectives, the nature of the factors (Xs) and responses (Ys), resources available, quality of the information to be developed and the theory available to guide the experiment design and analysis. A careful diagnosis of the experimental environment along these lines can have a major effect on the success of the experimental program.

Over the years, we have learned that experimentation can be used to improve all types of processes in manufacturing and service. As with any endeavor, it is important to have a strategy to guide your work.

Recognizing that experimentation is sequential—sometimes involving several phases—the SCO strategy has proven to be a high-yield strategy to guide experimentation.

The SCO strategy has stood the test of time, and it’s definitely worth your consideration.

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

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

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.