The use of statistical methods in industry is increasing. Arguably, the most cost-beneficial of these methods for quality and productivity improvement is statistical design of experiments. A trial-and -error search for the vital few factors that most affect quality is costly and time-consuming. The purpose of experimental design is to characterize, predict, and then improve the behavior of any system or process. Designed experiments are a cost-effective way to accomplish these goals.
JMP’s custom designer is the recommended way to describe your process and create a design that works for your situation. To use the custom designer, you first enter the process variables and constraints, then JMP tailors a design to suit your unique case. This approach is more general and requires less experience and expertise than previous tools supporting the statistical design of experiments.
Custom designs accommodate any number of factors of any type. You can also control the number of experimental runs. This makes custom design more flexible and more cost effective than alternative approaches.
This chapter presents several examples showing the use of custom designs. It shows how to drive its interface to build a design using this easy step-by-step approach: