How do I create a fractional factorial DOE with replicates in JMP 16?

Question

Hello all,

I am trying to create a fractional factorial balanced DOE matrix with 11 3-level factors and 27 conditions and 4 replicates per condition (5 runs per condition). How do I go about doing this in JMP 16? I tried using a custom design and specifying "4 replicates" and "135 runs" but I am not getting a matrix with replicates. Instead, I am getting 135 different runs. Below is the DOE data table I want to generate with 5 runs for each of the 27 conditions.

Mark_Bailey · Answer

JMP will build the design you want. The design is available in one of the older DOE platforms. Select DOE > Classical > Two-Level Screening > Screening. Define your 11 factors as Discrete Numeric with 3 levels. Click Continue and select the first radio button (fractional factorial), which is the default choice.

See this documentation and read about Placket-Burman designs.

Please see the important advice offered here. A successful experiment is more than how the design is made.

Here is an example with 11 factors with 3 levels. It is a P-B L27 design. Use the DOE Dialog table script to start at the beginning of the design process.

L27.jmp

statman · Answer

Here are my thoughts/questions/comments:

There are many questions before providing an appropriate response.  I apologize, I think it is appropriate to consider why you want the experiment you are planning rather than just say here is how you create it in JMP.  Feel free to ignore my thoughts if they are not useful.

It sounds like you have decided on 27 runs (treatment combinations)?  Is this a Taguchi L27 design you want?  Why?  You want to do a fractional factorial, but you are testing factors at 3 levels?  Why?  Are you trying to do a screening design? You will be confounding 2nd order linear effects with non-linear effects. This doesn't really follow hierarchy guidelines.  So this begs the question, what model are you trying to evaluate?  Do you want to understand causal structure or "pick a winner"?  Why 4 replicates?  Estimating quadratic and cubic effects of a replicate is non-sensical.  Can you get a reasonable estimate of noise using 2 replicates?  You want 5 repeats of each treatment, why?  Do you understand how you will use the repeats?

My advice is to think sequential experimentation.  First evaluate the relative importance of the 11 factors by exaggerating each of their effects equally.  Accomplish this by using 2-levels that are set boldly.  This creates a design space.  Determine if this space is appropriate.  If not, move the space (by changing factors or factor levels), if it is, augment the space (add levels, center points,  etc.).  replication and repetition are 2 excellent strategies to handle noise.  Repeats for short-term noise components (measurement, within sample, sample-to-sample) and replicates for long term noise components (raw materials, ambient conditions, etc.).  You might consider blocking as strategy for replication.  I would start with 2 extreme blocks.  Much depends on the noise you are trying to "evaluate" or whether you are trying to be robust to noise.

Sravya_J · Answer

Hi Victor,

Thanks for the detailed explanation. All my factors are categorical with three levels and identical cycle times were put together for ease of conducting the experiments. I asked Dan as well, but when I do a custom design like Dan suggested, a matrix which is not the matrix I want is generated. Could I simply change the entries in the generated data table to match my desired matrix and have the built-in DOE evaluation script run accurately?

Best,

Sravya