Design of Experiments with JMP^®

Use leading-edge tools for design of experiments

Most organisations rely on A/B testing for experimental design, a method for improvement that requires testing one situation against another with many factors in flux. This is a very slow way to learn about your business.

Design of experiments (DOE) offers a practical approach for exploring multifactor opportunity spaces that exist in almost all real-world situations. Using multifactor experiments, you can tease out the effect of an individual factor and hence learn more quickly at minimum cost. JMP offers leading-edge capabilities for optimal design of experiments. JMP also offers analysis in a form you can easily use and includes a rich set of modeling methods.

When you wish to create a design that also takes into account specific parameters like time, budget and other experimental limitations, the unique Custom Designer in JMP constructs a design to fit your problem (using an optimal design), so you don't have to fit your problem to a textbook design.

Evaluate Design platform lets you analyse any table treated as a design, change model and alias terms, and see updated diagnostics.

Hard-to-change variables, such as the temperature of an industrial oven or the location of a cornfield, exist in the real world. A completely randomised design might require such factors to be reset with each run. Clearly, this would be impractical or cost-prohibitive. The designed experiment most appropriate for such situations is called a split plot. JMP produces I-optimal split-plot, split-split and strip-strip designs. JMP generates the design and includes the appropriate random-effect restricted maximum likelihood (REML) model as part of the table that contains the experimental design. No other software package on the market offers this level of flexibility with split-plot designs.

JMP also supports classical screening (e.g., fractional factorial), response surface, full factorial, nonlinear and mixture designs, as well as advanced designs such as space-filling, accelerated life tests and choice.