Design of Experiments (DOE) is a structured approach for varying process and/or product factors (x’s) and quantifying their effects on process outputs (y’s), so that those outputs can be controlled to optimal levels.

DOE deals with identification of critical factors and their response variables, and the magnitude of the response for each level of the critical factors. DoE is also used to understand the interaction between the various critical factors to ensure right mix of the critical factors to get the best amount of response.

DoE is used to understand the transfer function and mathematical model for the optimization of the response variable.

A DC motor manufacturer might wish to understand the effects of two process variables, wire tension and trickle resin volume, on motor life. In this case, a simple two factor (wire tension and trickle resin volume), two level (low and high values established for each of the two factors) experiment would be a good starting point. Randomizing the order of trials in an experiment can help prevent false conclusions when other significant variables, not known to the experimenter, affect the results. There are a number of statistical tools available for planning and analyzing designed experiments.