Taguchi, Factorial, RSM, ... -- Which DOE is Best?

Design of Experiments is implemented in many strategies, including Taguchi, Factorial, and Response Surface Methodology. Read how Design of Experiments is the same tool in these different strategies, and get an overview of these different strategies. Design of Experiments (DOE) is an elegant technique for efficient, thorough, objective experimentation. It has been around since the 1920's and has been used successfully in industry since the 1950's. This technique is so fundamental that it has been used in numerous experimental strategies -- often with the goals of the particular strategy being mistaken for DOE itself. Design of Experiments includes these features wherever it is used: A mathematical model is chosen to describe the situation under study. An experiment design is created to collect data in the best way to fit these data to this model. The data are collected and analyzed using the model initially chosen. As an analogy, think of the "model" as a "recipe." You use this to create a "design" or "shopping list." Without the recipe, you can only guess what ingredients you may need. Without a model, you can only guess which experiments to run. "Analyzing" the data is like putting away the groceries in an organized manner. So why does DOE have so many names? Actually, it doesn't -- it just appears to. The names come from various strategies that use DOE. Here is a list of some of the most popular experimental strategies and their goals: Taguchi is interested in finding a "robust" answer to the experimental question. It seeks an answer that is insensitive to factor variations and noise. It doesn't predict the best combination of factors to achieve your goals. "Factorial", or "Classical DOE," was the first technique used with designed experiments. It allows you to see which factors are most important and helps you to identify important interactions among the factors. It doesn't predict the best factor levels to meet your goals. Response Surface Methodology (RSM) uses your model to make contour plots of predicted behavior. Using these plots you can actually predict the best combination of factors to meet your goals. All of these strategies use DOE. The way DOE is applied will differ depending on the goals of the strategy, but the DOE technique does not change. Objective Design of Experiments workshops will teach you to use DOE in your work. Design of Experiments is a fundamental technique for industrial experimentation. You will learn to apply DOE easily without excessive math and theory. We will help you be even more successful!