Design of Experiments (DOE) is a structured method for planning, executing, and analyzing tests in which selected input factors of a process or product are intentionally varied to observe and quantify their effects on one or more measured outputs.
In a manufacturing or process context, DOE typically includes:
- Defining the objective of the study (for example, reducing a defect rate or stabilizing a critical dimension).
- Selecting the input factors to vary (such as temperatures, speeds, pressures, material lots, or setup parameters) and specifying the levels or settings to test.
- Choosing an experimental layout (for example, full factorial, fractional factorial, or response surface designs) that dictates which factor combinations will be run.
- Randomizing and, where applicable, blocking runs to separate factor effects from known or suspected sources of variation.
- Conducting the trials according to the plan while recording the defined output responses (such as yield, dimensional results, or cycle time).
- Analyzing the collected data with statistical methods to estimate main effects, interactions, and, when relevant, curvature in the response.
- Interpreting which factors and factor combinations are statistically associated with changes in the measured outputs and using those findings to adjust or refine process settings.
Within Root Cause Analysis and other investigative activities, DOE is used as a formal way to test hypotheses about potential causes by imposing controlled changes on the process and examining the resulting data.