Planning phase

Why is planning an experiment important?

Because resources are limited, it is very important to get the most information from each experiment you do.

Well-designed experiments can produce significantly more information and often require fewer runs than haphazard or unplanned experiments.

Also, a well-designed experiment will ensure that you can evaluate the effects that you have identified as important.

For example, if you believe that there is an interaction between two variables, be sure to include both variables in your design. Interactions are impossible to estimate from a "one factor at a time" experiment.

An interaction occurs when the effect of one variable is affected by the level of a different variable.

Careful planning can help you avoid problems that can occur during the execution of the experimental plan. For example, personnel, equipment availability, funding, and the mechanical aspects of your system can affect your ability to do the experiment. If your project has low priority, you might want to do small sequential experiments. That way, if you lose resources to a higher priority project, you will not have to discard the data you have already collected. When resources become available again, you can continue experimentation.

Plan an experiment

How much you prepare before starting experimentation depends on your problem. You might want to go through the following steps:
Define the problem
Developing a good problem statement helps ensure you are studying the correct variables. At this step, you identify the questions that you want to answer.
Define the goal
A well-defined goal will ensure that the experiment answers the correct questions and yields practical, usable information. At this step, you define the goals of the experiment.
Develop an experimental plan that will provide meaningful information
Be sure to consider relevant background information, such as theoretical principles, and knowledge obtained through observation or previous experimentation. For example, you might need to identify which factors or process conditions affect process performance and contribute to process variability. Or, if the process is already established and you have identified influential factors, you might want to determine optimal process conditions.
Ensure the process and measurement systems are in control
Ideally, both the process and the measurements should be in statistical control as measured by a functioning statistical process control (SPC) system. Even if you do not have the process completely in control, you must be able to reproduce process settings. You also need to determine the variability in the measurement system. If the variability in your system is greater than the difference/effect that you consider important, experimentation will not yield useful results.

Minitab provides numerous tools to evaluate process control and to analyze your measurement system.

Screening phase

In many process development and manufacturing applications, the number of potential variables (factors) is large. Screening (process characterization) is used to reduce the number of factors by identifying the most important factors that affect product quality. This reduction lets you concentrate process improvement efforts on the few most important factors. Different types of screening designs can screen different types of terms and detect or model curvature. If necessary, further optimization experiments can be done to model more complex interactions or to more precisely define the nature of the response surface.

The following designs are often used for screening:
  • Definitive screening designs can estimate complex models for a small number of important factors that were in an experiment with many factors.
  • 2-level full and fractional factorial designs are used extensively in industry.
  • Plackett-Burman designs have low resolution, but their usefulness in some screening experimentation and robustness testing is widely recognized.

Optimization phase

After you have identified the important terms by screening, you need to determine the optimal values for the experimental factors. Optimal factor values depend on the process goal. For example, you might want to maximize process yield or reduce product variability.

Verification phase

Verification involves performing a subsequent experiment at the predicted optimal conditions to confirm the optimization results. For example, you can do a few verification runs at the optimal settings, then obtain a confidence interval for the mean response.

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