Design of experiments (DOE) is used to understand the effects of factors and interactions that influence the output of a process. It is designed to systematically build understanding and enhances how predictable a process is. DOE investigates a list of potential factors which could be derived from a variety of sources such as process maps, FMEAs, Multi-Vari studies, Fishbone Diagrams, brainstorming techniques, and Cause and Effect Matrices, whose variation might impact the process output. In typical data-analysis method, what happens in a process is observed without intervening. With a designed experiment, the process settings are changed to see its effect on the process output.
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Design of experiments refers to the structured way you change these settings so that the effects of changing multiple settings can be studied simultaneously. This active approach allows for effective and efficient exploration of the relationship between multiple process variables (x’s) and the output, or process performance variables (y’s). DOE is commonly used in the Analyze step of the DMAIC method as an aid in identifying and quantifying the key drivers of variation, and in the Improve step as an aid in selecting the most effective solutions from a long list of possibilities.
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Situations, where experimental design can be effectively utilized include the following:
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