The tutorials are good for understanding why to use multi-factor experiments and I agree these would be a good place to start. Unfortunately, they lack a thorough discussion about how to handle noise. The question of randomization requires a complete understanding of the options available. These are difficult to document (or include in a tutorial) as they are often situation dependent.
Regarding the "path" of sequential experimentation, it is impossible to know what you will learn from the first experiment or to know what the second experiment will be. Whether you find 2, 3 or 4 or no factors significant in the first screening experiment is dependent on:
1. Having a measurement system with appropriate discrimination,
2. Setting factor levels bold, but reasonable enough to expose the factors effect,
3. The "truth" (what we are trying to uncover) those factors actually matter,
4. Whether the noise is truly representative of the future noise in the extraction process.
The "test" for factor significance is typically made against all "other" factors that change during the experiment (including noise) or as is often suggested against the noise estimated by randomized replicates. If the "other" factors have a larger affect, you will conclude the factor of interest is insignificant and you will likely abandon that factor (or at least not continue to study it). If you minimize the noise in an experiment (e.g., hold noise constant or run the experiment over a time period where the noise does not change) you could easily conclude a factor is significant only to learn later that its effect depends on the noise.
The purpose of the first experiment, in a sequential approach is to find the factors that are worthy of further investigation. All of the factors you are experimenting on should be supported by scientific hypotheses. You should consider and predict ALL possible outcomes. If a factor is not significant, what are those ramifications? If a factor is significant, what will the next set of work look like? Will it be considering non-linear effects, how that factor interacts with other factors, other higher order effects, etc.? A second experiment is only one option (for example, you might consider sampling as the experiment will not provide much insight into consistency). And whether the second experiment is a central composite design or some other design can only be predicted at this point. You won't know what to do until after you have analyzed the first experiment ant thought about the scientific implications of that knowledge. "There is no instant pudding", Deming.
"All models are wrong, some are useful" G.E.P. Box
