Design of experiments (DOE) is an approach used in numerous industries for conducting experiments to develop new products and processes faster, and to improve existing products and processes. When applied correctly, it can decrease time to market, decrease development and production costs, and improve quality and reliability.

DOE is much more rigorous than traditional methods of experimentation such as one-factor-at-a-time and expert trial-and-error. This rigor allows practitioners to explicitly model the relationships among the numerous variables in any system, make more informed decisions at each stage of the problem-solving process, and ultimately arrive at better solutions in less time.

DOE is a powerful method that can seem deceptively easy, but in reality it takes significant know-how to make it work reliably. Most unsuccessful attempts to apply DOE can be attributed to one of a handful of pitfalls. In addition to knowledge of statistical methods, the keys to making it work are discipline and effective communication between the statistician and the scientists, engineers, and managers on the project team who best understand the product or process.

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