Why use DOE and the Guided mode of Easy DOE introduced in JMP 17?
- More rapidly answer “what if?” questions
- Identify important factors when faced with many
- Do sensitivity and trade-space analysis
- Optimize across multiple responses
- By running efficient subsets of all possible combinations, one can – for the same resources and constraints – solve bigger problems
- By running sequences of designs one can be as cost effective as possible and run no more trials than needed to get a useful answer
https://www.youtube.com/watch?v=cjPhaWcjXNg
See how to:
- Start with the end by presenting DOE results interactively to decision makers
- Use the 6-step DOE Process implemented in the Easy DOE interface
- Understand factor types supported and model choices
- Use Guided Easy DOE process for slightly more complex 3-response, 4-factor, trade-space/optimization example using new .jmpdoe file.
- Define
- Specify
- Design
- Enter Data
- Analyze
- Predict
- Report
Questions answered by @tom_donnelly and @O_Lippincott during the live webinar:
Q: if you are trying to detect a treatment effect on a process and have some prior knowledge on variation, is there a way within easy DOE to employ a power analysis for optimizing sample size?
A: Easy DOE does not have a power analysis within the platform but if you export the data on the data entry tab, there is a script in the resulting data table for design evaluation which includes power analysis. You can find various power analysis functions under the main DOE heading.
Q: Is there a way to visualize the design graphically?
A: Under the data entry tab, there are factor plot graphs that show each individual factor. The prediction profiler is also a way to visualize the effects of all the factors together.
Q: Is there information about what type of design was generated through this platform? For example, if it was an optimal design (and which optimality criterion was used), or if it was a fractional factorial (and if so what was the generator), etc. Basically, where can we find the "behind-the-scenes" info?
A: Here are the Easy DOE selection rules and how Easy DOE chooses each type of design.
Q: Is there information about what type of design was generated through this platform? For example, if it was an optimal design (and which optimality criterion was used), or if it was a fractional factorial (and if so what was the generator), etc. Basically, where can we find the "behind-the-scenes" info?
A: Easy DOE constructs designs using either the Custom or Definitive Screening Designs.
Main Effects: Custom Design (D-Optimal)
Main Effects (Uncorrelated with Two-Factor Interactions): Definitive Screening Design or Custom Design: (Alias Optimal)
Main Effects (Including all Two-Factor Interactions): Custom Design (D-Optimal)
Response Surface Design: Custom Design (I-Optimal)
Q: When using the profiler after fitting a multi-factor model, could you comment on what, if any, differences in modeling take place if you 'fit together' vs. 'fit separately'?
A: I'm interpreting your question as fitting the multiple responses in one profiler versus as 3 separate profilers. When you have all 3 responses in one profiler, you can look at the trade off in factor settings between the predicted responses of the 3 responses. The modeling is the same, but the optimization is different.
Q: In a manufacturing process, there may be multiple factors that go into assembly. There may be a factor that can be eliminated. How do you use DOE to identify this factor that can potentially be eliminated from the MFG assembly process?
A: When analyzing the data from a DOE, you would see the factor that can be eliminated will not be statistically significant as having an effect on the response. In the profiler that Tom showed, the effect would be a flat line versus having a slope
Q: How do you check for data reliability and/or does the easy DOE spit out Cronbach's alpha?
A: Easy DOE does not include Cronbach's alpha. That can be found in JMP's Multivariate platform using the Item Reliability option that each show or hide an item reliability report. The reports indicate how consistently a set of instruments measures an overall response, using either Cronbach’s α or Standardized ). See Item Reliability.
Q: Our study easily have more than 30 factors in the system. Our main goal for the DOE is trying to understand if there is any interations between this 30 factors. Which model is best to choose?
A: The second and third model options both will see if there are any interactions. It can be a difference in the number of runs needed and the trade off in how "expensive" in time or money each run costs.
Q: What is the difference between first order and second order models?
A: When we have a large number of factors, and we just want to find the critical few and investigate those critical few with more complexity, we would move from the first order model up to a second order model where we're trying to make predictions.
Q: Where is the simulator that you plug in the data
A: I added the simulator to the data table that I used as part of the demo. But there is also this simulation panel. If you know the coefficients, you can plug them in. So the short answer is, for the simulator built into JMP is a great for quick checks to find if this model will be able to be analyzed. If you're truly trying to simulate a process, you need to get your knowledge of the model fit and those coefficients into JMP.
Q: So by default, no replicates are added? Do we need to add them separately?
A: You can add replicates using the Flexible (more advanced) Mode.
Flexible Mode lets you add replicatesFlexible Mode lets you add replicates
Q: In many of the experiments that are ran by my team, many of the continuous factors are ran as discrete numeric factors. Are there any advantages and disadvantages for using this approach?
A: The only advantage would be, some people want more intermediate levels. If you're ultimately going to fit a quaradratic model, all you need are 3 levels. For example, to force 5 levels and you just pick a discrete numeric and give it 5 levels, JMP will probably put them in. Then JMP will pick way more of the extremes and fewer in the middle, because mathematically it's still thinking continuously with it. There is also more leverage at the extremes with categoricals. In summary that is a way to force more levels for a continuous, but if it's truly continuous, probably you don't need to do that.
Q: I use another DOE software that usually contain a set of lack-of-fit runs in a design. How was lack of fit tested in JMP DOE design?
A: Replicates will always give you a lack of fit test, because then we have an estimate of pure error that can be compared to the model error. If you don't have any replicates, we don't give you a lack of fit test but give you the same model that it will have some model error. If you've been making the same product for years, you probably already have a good historical idea of what the pure error is. One reason to do replicates nad tget the lack of fit test is if you are new to the process.
Resources
- JMP documentation on Easy DOE.
- Mark Baily's Developer Tutorial on Easy DOE.
Sean,
My short answer is that it appears you already know a fair amount about DOE and have ideas about customizing what you want! As a result, the default Easy DOE Guided solution is not fitting your expectations and you want to modify it. You wanted an 8-run design instead of the default 12 run design. That's why Easy DOE has a Flexible Mode that I plan to discuss this fall in a Mastering JMP webcast, and JMP has the Custom DOE platform that you have almost certainly used in JMP 16.
With respect to your questions,
Q1. Details about confounding levels can be found by Exporting Design and clicking on Green Triangle next to Evaluate Design in the Table Panel at the upper left of data table. Below is likely the result you will get.
Q2. Right now, the Easy DOE platform does not erase the original design it creates when you click the design tab or the navigation control forward button again - unless you change your choice of number of runs. You can simply change the 8 to a 9 and it will create a new design and then change the 9 back to an 8 and you will get a different design than your original 8. At least the run order will be different.
Q3, As you mention, you can use Flexible mode to add a center point to your 8-point design (Taguchi L8) as shown below:
Note the total User Specified runs is 9 with 1 Center Point resulting in design below:
I would suggest that if you know what you want and it is not the default result of the Guided DOE platform, then investigate if you can achieve what you want in the Flexible platform or go to the Custom DOE platform where you may have even more control. I hope this gives you what you need to get more out of JMP's various DOE choices.
Good luck experimenting with JMP.
Tom
@tom_donnelly @gail_massari
Also, for the response part, all identified response might be provided at the beginning of this EasyDOE. As another response (e.g. life cycle after the continuous usage of the run group - built by each DOE design run) becomes available, user may want to add one more response or replace an existing one by overwriting the response of the same target/importance for the existing DOE analysis. Q4. Can it be allowed? Or should it be rerun completely - maybe being ended up with a different DOE design layout of different combinations of factors, which I am struggling with it right now, making the existing runs useless?
@tom_donnelly @gail_massari
Hi Tom,
I was using JMP16 without realizing EasyDOE is a new feature on JMP17. So, I just installed 17 right after the webinar. I try to utilize this feature for my existing DOE (by Taguchi L8 with confounding level IV by 4 factors of 2 levels each - Q1. Would JMP specify/hint what levels of confounding its DOE design will be and choose an option for it per confounding level?). When I designed DOE table by specifying 8 runs for (Main Effects & Uncorrelated with 2-factor interactions), the DOE design of the first try was exactly matched to my original Taguchi L8 design - although its order was mixed up. However, as I repeat the DOE designing part again, Q2. it does not allow re-running of DOE design part by removing the previous build. Can it be done somehow? (If I add one more response in Define part, it does maybe since its structure is changed.). In Taguchi L8 design, I put a center point of centering all 4 factors to check possible non-linearity not in DOE analysis but in fit-modeling phase. If allowed, Q3. Is there any way I can customize the DOE design by adding one/some more DOE design point(s)? I know you answered for the question of repeat run addition using flexible model in Data Entry. Thanks for your answers in advance. /Sean
