Welcome to the JMP Community.
While I agree with Mark , that you should consider sequential experimentation, I would not choose Plackett Burman or DSD. I would choose a fractional factorial so you can easily know the aliasing (and there is no partial aliasing).
Some questions/comments:
1. How many factors are you considering for screening?
2. Ensure you choose bold level setting to increase the likelihood the factors will demonstrate their effects.
3. Make a list of predicted effects (main effects and 2-factor interactions). Prioritize this list. If you have 2-factor interactions high on the list, consider higher resolution designs, if they are at the bottom, Res. II is adequate.
4. The most challenging part of experimentation is not the matrix for factors, but the strategy that you use for handling noise. If you cannot identify the noise is in the extraction process, then you would randomize to prevent the noise from being confounded with factor effects (and potentially to get an unbiased estimate of the noise). If you are able to identify the noise variables are in the extraction process (using tools such as a process map), then your options are better (RCBD, BIB, Repeats, split-plots, covariates, etc.) To quote G.E.P. Box:
"Block what you can, randomize what you cannot"
"All models are wrong, some are useful" G.E.P. Box