Design Of Experiments

For organizations to gain and maintain a competitive advantage, there’s a constant need for the deployment of new products on the market. This is a risky endeavor since there is no guarantee that it will succeed. And the risk is compounded when factoring in the change the organization needs to make to its current process.

Sure, the organization can turn to the various forms of product testing, but the costs of doing so aren’t cheap. Furthermore, product testing reveals potential problems in the later stages of development. What if there was a less costly and low-risk way for organizations to design new products that will not only meet but exceed the consumer’s expectations without product testing? That is where Design of Experiments (DoE) comes in.

In Six Sigma, DoE is a tool for performing statistical analysis on a process’s data to identify the effects inputs have on the output. The output in this scenario is the product. To be conversant in DoE, one needs to know the following basic DoE terminology:

  • Factor: The input variable
  • Run: The experiment
  • Levels: The values a factor will take as the experiment is being run
  • Response: The result of the experiment or run, which is based on adjusting the input variables by two or more levels (usually, it is up to three levels)

Design of Experiments helps project teams analyze the data to determine the cause-and-effect relationship between inputs and output that lies beneath the surface. It essentially minimizes the need for conducting multiple lengthy and costly product tests. This is because the project team can quickly and accurately identify which factors have the most effect on the final product.

DOE is mainly used in the Improve phase of the DMAIC methodology. It allows project teams to develop multiple what-if scenarios. That way, they can figure out which inputs will lead to the optimum output.

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