General multi level DOE (AAAARRRGGHHHHHH!!)

Two factors at 6 levels will be a 6**2 experiment for a total of 36 experiments.  I’m not aware of any package that will do this for you.  The easiest way to set this up is to take a piece of graph paper and simply plot out the 36 points that would be part of the 6×6 matrix.  Before doing any of this however, I would recommend asking some hard questions concerning the need for 6 levels.  In  the vast majority of cases this is definitely overkill.  The operating philosophy behind DOE is that if change is going to be observed it will best be seen by contrasting extremes-hence the focus on 2 and 3 level designs. 
  If your circumstances are such that you will not be premitted to consider less than 6 levels per factor, I’d recommend arguing for a 3 level “screening” design over the same region.  This would give you a 9 point design and with a couple of replicates you would have 11 experiments which would permit a check of all interactions and all linear and curvilinear effects.  It is true that with such a design only the corner points would correspond exactly to points from a 6 level design but I’d have a hard time believing that the small difference between the other points of a three level design and those of a 6 level design would make that much difference. Thus, if there was still some doubt you could use the 3 level design as a starting point and then fill in other areas of the design with points from the 6 level design.  Since you could use your regression equation to predict the responses at the levels of the 6 factor design. the additional design points would act as confirmation runs for the findings from your initial effort.
 

Robert,
Forgive me.  My head is not on straight (a Monday morning thing).  You are right, it is 36 experiments, not the 64 I just suggested.
Scott
 

Tell me how many runs you can afford—-I will send you and DOE matrix. I have a software that can generate DOE matrix with any number of levels. It is called LHS.

WOW – more than 6000 experiments……….
Stat>DOE>Factorial>Create>General>No. factors>Designs>No. levels
Are you sure you are not going to make som fractional or screening,this is a big (expensive) experiment.
Jan

Robert,
Minitab allows you to input your own generator. Im have never had to do this in practice. If I have a need for optimal designs or something not in Minitab, I use Design Expert
But, I wonder why in the world 6 and 8 level designs are even being contemplated. Some years ago I worked in a fiber optic fab facility. On some new product designs the designers (mostly PhD level physicists) wanted five and six levels  DOE. They believed that the responses were  theoretically complicated quartic and above curves.
We did quite nicely with highly fractionated 2 level designs with center points, followed by response surface measurements.
 In general, even if the physics and engineering says the response are higher order curves, over the range of actual use, they tend to be adequately modeled by at most third order equations. This I believe is the difference between theory and practice.
 
 

Why not use SAS or SPSS? Both can run this volume of data. HF Chris

Many have suggested reducing levels but none of us know what he is measuring.  Are six levels per factor that hard to measure…. depends on the study.  Are six levels needed… depends on the study. Could there be an interaction missing if one level is not understood… yes.  The question was how to analyze this project with software.  
Why do we reduce studies in size? Money? Time? Quick payback? How often do we miss the mark if we had just dug a little deeper.  Don’t get me wrong because a believe in the process but also find many limiting factors in it.  I see reports of .88 r2 and alpha of .05 but then see how the data can be biased and misconstrued, reporting findings not capable unless you have controlled many variables. In many cases an r2 of .68 is great when reached.  Just a little perspective often missed.
HF Chris
 
 

The problem with prescreening is also the same problem with cause and effect root cause analysis, you have to truly understand the system. For example, I worked with composites and adhesives where humidity and temperature interaction is critical. If you did an a priori study with the wrong combination this could be a disaster when introduced as the right thing to do in production. I also agree with you that people fail in DOE because they do not due their homework. The key is to identify the critical measurements that effect their process…. but this can turn out to be the chicken or the egg question, what do you need to know first.HF Chris