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Jun 25, 2020 8:10 AM
(1034 views)

Hi All,

Assume we have 3 factors with 2 levels each. If I understood it correctly, for small number of factors we can consider a full factorial design, in this case would be 2^3 full factorial design. What should we do, if by some reason, 2 out of 8 conditions is not possible to run? Is there any way/concept to handle this situation?

Thanks in advance!

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You can use the custom designer to deal with cases where you need to exclude specific combinations:

-Dave

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Just to add a bit to @David_Burnham 's advice, if you use the Custom Design platform, you also have flexibility wrt to the # of runs, replication, effect estimation, and other design attributes/characteristics so you'll probably want to give some thought to these as well. For example, the 2**3 full factorial design allows for estimation of all possible main effects, two factor and single three factor interaction effects. Do you really need to estimate all of these effects? Perhaps you don't care about one or more? You can articulate the specific model that you're interested in fitting as you plan the design, then the search algorithm will provide an (probably D?) optimal design tailored to your specific requirements.

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You can use the custom designer to deal with cases where you need to exclude specific combinations:

-Dave

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Re: DoE: What to do if some conditions of a design are almost impossible to run?

Thank you David, these all are very useful.

Highlighted
Just to add a bit to @David_Burnham 's advice, if you use the Custom Design platform, you also have flexibility wrt to the # of runs, replication, effect estimation, and other design attributes/characteristics so you'll probably want to give some thought to these as well. For example, the 2**3 full factorial design allows for estimation of all possible main effects, two factor and single three factor interaction effects. Do you really need to estimate all of these effects? Perhaps you don't care about one or more? You can articulate the specific model that you're interested in fitting as you plan the design, then the search algorithm will provide an (probably D?) optimal design tailored to your specific requirements.

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Re: DoE: What to do if some conditions of a design are almost impossible to run?

Thanks for your informative response. This will help me a lot.

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Some questions for a different perspective...Why are those particular treatment combinations impossible? How do you know they are impossible?Can you provide more context? Are the levels too bold? Are the levels you set for some factors dependent on the levels of other factors (potentially factors nested within others vs. crossed)?

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Re: DoE: What to do if some conditions of a design are almost impossible to run?

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Re: DoE: What to do if some conditions of a design are almost impossible to run?

It was just a question of curiosity, which makes it not easy for me to formulate it in a specific way. But let's assume these factors depend on the levels of other factors. This would be an interesting case.

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Re: DoE: What to do if some conditions of a design are almost impossible to run?

@AzizaWhenever I was on teams and we were planning experiments, whether we used a 'catalog of designs' approach (for example, a plain Jane 2**3 full factorial design) or an optimal design approach...whenever we had a proposed experimental design, we'd ask some questions like:

1. Will any of these combinations cause a known safety, or otherwise known or suspected hazardous condition that might 'break something' or worse yet, have a potential to cause personal injury or harm?

2. Given this design, are there other nuisance factors or experimental constraints we need to consider? If so perhaps we need to add some thinking around blocking (say, we've got to use 2 different suppliers for raw materials...and we want to guard against supplier to supplier variability) or, maybe we've got some 'hard to change' factors and it makes logistical sense to group the levels together (then a split plot approach...a form of blocking should be considered).

3. Do we need to consider response testing variability issues for the objects coming out of the experiment?

All these answers can influence the final design choice.