The DOE Workflow: Describe, Specify, Design

The DOE platforms are structured as a series of steps that present the workflow that is intrinsic to designing experiments. Once you complete each step, you click Continue to move to the next step. The elements described in this section are common to nine of the design of experiments platforms. These are the platforms that are addressed in this section:

•	Custom Design

•	Definitive Screening Design

•	Screening Design

•	Response Surface Design

•	Full Factorial Design

•	Mixture Design

•	Covering Array

•	Space Filling Design

•	Taguchi Arrays

Three special-purpose platforms differ substantially: Choice Designs, Accelerated Life Test Design, and Nonlinear Design. These three platforms are not addressed in this section.

This section describes the steps in the DOE workflow. It also discusses their implementation in the various design platforms.

1.	Define Responses and Factors.

2.	Specify the Model

3.	Generate the Design

4.	Evaluate the Design

5.	Make the Table

Define Responses and Factors

In the Describe step of the experimental design framework:

•	You identify the responses and factors of interest.

•	You determine your goals for the experiment. Do you want to maximize the response, or hit a target? What is that target? Or do you simply want to identify which factors have an effect on the response?

•	You identify factor settings that describe your experimental range or design space.

When they open, most of the JMP DOE platforms display outlines where you can list your responses and your factors. The Responses outline is common across platforms. There you insert your responses and additional information, such as the response goal, lower limit, upper limit, and importance.

The Factors outline varies across platforms. This is to accommodate the types of factors and specific design situations that each platform addresses. In certain platforms, once responses and factors are entered, a Define Factor Constraints outline appears after you click Continue. In this outline, you can constrain the values of the factors that are available for the design.

Responses and Factors for Box Corrosion Split-Plot Experiment shows the Responses and Factors outline using the Custom Design platform for constructing the design in the Box Corrosion Split-Plot.jmp sample data table, located in the Design Experiment folder. Also shown is the Define Factor Constraints outline, which appears once you click Continue. The Define Constraints outline enables you to specify restrictions that your factor settings must satisfy.

Responses and Factors for Box Corrosion Split-Plot Experiment

Specify the Model

Once you have completed filling in the Responses and Factors outlines, click the Continue button. This brings you to the next phase of design construction, where you either explicitly or implicitly choose an assumed model.

The Custom Design platform enables you to explicitly specify the model that you want to fit. The design that is generated is optimal for this model. The other design platforms do not allow you to explicitly specify your model. For example, in the screening platform, one option enables you to choose from a list of full factorial, fractional factorial, and Plackett-Burman designs. The aliasing relationships in these designs implicitly define the models that you can fit.

In Custom Design, when you click Continue after filling in the Responses and Factors, you see the Model outline. An example, for the design used in the Box Corrosion Split-Plot.jmp sample data table, is shown in Model Outline for Box Corrosion Split-Plot Experiment. The assumed model requires that the Furnace Temp and Coating main effects, and their interaction, be estimable. The design that is generated guarantees estimability of these effects.

In most other platforms, clicking Continue gives you a collection of designs to choose from. In Full Factorial, Continue takes you directly to Output Options, since the design is determined once the Factors outline is completed.

Model Outline for Box Corrosion Split-Plot Experiment

Generate the Design

Most of the DOE platforms give you some control over the size of the final design. In Custom Design, you can specify the number of runs and, when appropriate, the number of center points and replicate runs. In other platforms, you have various degrees of flexibility. Often you can specify the number of center points, replicate runs, or replicates of the design.

Once you have specified your options in terms of the number of runs, click Make Design. The DOE window is updated to show your design in a Design outline.

The Design outline for a 24-run custom design for the Box Corrosion Split-Plot.jmp experiment is shown in Design Outline for Box Corrosion Split-Plot Experiment. Because Changes for Furnace Temp was specified as Hard, a Whole Plots factor is constructed to represent the random blocks of settings for Furnace Temp.

Design Outline for Box Corrosion Split-Plot Experiment

Evaluate the Design

When you click Make Design, in most platforms, a Design Evaluation outline appears. Here you can explore the design that you created in terms of the following: its power to detect effects, its prediction variance, its estimation efficiency, its aliasing relationships, the correlations between effects, and other design efficiency measures. The Design Evaluation outline for a Custom Design is shown in Design Evaluation Outline in Custom Design. Design Evaluation is covered in the Evaluate Designs section.

For some platforms, other types of design diagnostics are appropriate. For example, Space Filling Design provides a Design Diagnostics outline with metrics specific to space-filling designs. Covering Array provides a Metrics outline with measures that are specific to coverage.

Design Evaluation Outline in Custom Design

Make the Table

Most platforms provide an Output Options node or panel. Depending on the platform and the design, you can use the Output Options panel to specify additional design structure. For example, you can specify the number of runs, center points, replicates, or the order in which you want the design runs to appear in the generated data table.

The Output Options panel shown in Output Options Panel for Wine Experiment is for the experiment in the Wine.jmp sample data table, located in the Design Experiment folder. In this example, you can choose various Run Order options and construct the design data table. Or, you can choose to go Back and restructure your design.

Output Options Panel for Wine Experiment