Screening
overview      

In many process development and manufacturing applications, potentially influential variables are numerous. Screening reduces the number of variables by identifying the key variables that affect product quality. This reduction allows you to focus process improvement efforts on the really important variables, or the "vital few." Screening may also suggest the "best" or optimal settings for these factors, and indicate whether or not curvature exists in the responses. Then, you can use optimization methods to determine the best settings and define the nature of the curvature.

The following methods are often used for screening:

·    Two-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

·    General full factorial designs (designs with more than two-levels) may also be useful for small screening experiments