Hello @LogitPorcupine1,

If you have several factors in your system, I don't see why you wouldn't use DoE instead of OFAT approach.

DoE has many benefits compared to OFAT, here are some :

- Limited (and planned/controlled in advance !) number of experiments,
- Interactions and non-linear effects can be studied,
- Mathematical model to explain or predict response(s) variations,
- High efficiency (information vs. number of experiments),
- Errors are shared equally/homogeneously in the design space,
- Possibility to create constraints/customs designs
- Iterative process through "Augmentation" and a lot of strategies possible : you can start by screening main effects, then checking interactions, and finally build a robust Response Surface Model for predicting accurately your responses, ...

There are a lot of ressources to learn DoE with JMP, here are some :

Design of Experiments Intro Kit | Getting Started with JMP

Statistical Thinking (STIPS) - Free Online Statistics Course | JMP

Design of Experiments (DOE) Course | JMP

Design of Experiments | JMP

You can also check this very nice article on Nature about the benefits of using DoE in order to avoid "blank spots/area" in your experimental space (Figure 2) : A Design of Experiments (DoE) Approach Accelerates the Optimization of Copper-Mediated 18F-Fluorinat...

Hope this will help your reflexion and understanding,

Victor GUILLER

Scientific Expertise Engineer

L'Oréal - Data & Analytics