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