On the Study of Saturated and D-Optimal Saturated Designs for Two-Level Factorial Experiments

We contemplate an experimental situation in a $2^k$-factorial experiment with acute resource crunch so that we need to conduct just a saturated design [SD] - with the understanding that precision of the estimates cannot be estimated from the data. It is known beforehand which effect(s)/interaction(s) are likely to be negligible. We examine the flexibility to the extent that an experimenter can make a choice of an SD in order to retain information on all the remaining [non-negligible] effects/interactions.