This seems obvious, but more experiments go wrong at this point than at any other. Second, the experiment must consider ALL of the factors that may contribute to the outcomes of concern. To make the design simpler and to save money. Critical factors were thus left out, and the experiment did not discover the best answer. This is no longer necessary.
Custom computed designs are both optimal and minimal.
The measures most useful to our customers are log condition number, VIF's, and the I or D optimality. Log condition number tells you how well the design can be analyzed. Large log condition number means big possibility for numerical errors when performing the regression. As you know, a log condition number of 5 or less is best. VIF's measure the amount of cross-contamination in the b coefficients.