Design of experiments
(DOE) is the most efficient approach for organizing experimental work. DOE selects a diverse and representative set of experiments in which all factors are independent of each other despite being varied simultaneously. The result is a causal predictive model showing the importance of all factors and their interactions. These models can be summarized as informative contour plots highlighting the optimum combination of factor settings. DOE is used for three primary objectives:
Which factors are most influential and over what range?
How can we find the optimum settings taking into account conflicting demands of different responses?
Once the optimum is found, can we guarantee robustness close to that point or do we need to change specifications to achieve robustness?
Let's continue with a comparison of two experimental situations, with and without DOE.