Design of Experiments (DOE) Resource Center

What You Need to Know About Design of Experiments

Design of Experiments, or DOE, is one the most powerful, yet least understood and used, of the improvement tools available to manufacturing organizations. The financial payback period achieved from using DOE, especially screening experiments, is often measured in months and weeks, not years. What other investment in time and resources can generate that level of return over and over again?

DOE’s Require Planning

1. Design and Communicate the Objective

The objective will generally be one of three forms: The “Biggest” (to maximize the response), the “Smallest” (to minimize the response) or the “Closest-to-Target” (to hit a target)

2. Define the Process

Define the boundaries of the process to be experimented upon. This could be just internal processes or it could include the full extended process in which the processes of suppliers and/or customers are studied along with internal processes.

3. Select a Response and Measurement System

Responses are the outputs, or the dependent variables, of the process. In analyzing a designed experiment, you can use as many responses as you are willing to measure. A good measurement system is one that is accurate, repeatable, reproducible, stable, and linear. Taking good samples is a critical aspect of the measurement system. The samples from each experimental run must be representative of the response during that run.

4. Ensure that the Measurement System is Adequate

Make sure the measurement system has been calibrated. If the measurement system is not repeatable and reproducible, the results of the designed experiment will not be valid. It is prudent to conduct a GR&R before investing in the time, effort and funds for conducting a designed experiment.

5. Select Factors to be Studied

Factors are the independent variables that will affect the response; select those factors that should have the greatest impact on the response. Ensure that it is practical, feasible, and cost effective to select a factor to be studied and to change its level.

6. Select the Experimental Design

The type of design is highly dependent on the number of factors to be studied. Screening experiments are usually the best design choice early in an experimental sequence when many factors are to be explored.

7. Set Factor Levels

Be bold and set the levels at the edges of the operating window for the process when conducting screening experiments.

8. Final Design Considerations

Final considerations include: Selecting the experimental matrix to use; deciding how to estimate the experimental error and planning the experiment so that any external sources of variation are minimized.