Process / pipelineEngineering methods
Design of Experiments — DOE
Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.
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Sources
- Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗
- Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
Related methods
Referenced by
Bayesian Design of ExperimentsBayesian Quality Function DeploymentBayesian Taguchi methodBox-Behnken DesignCentral Composite DesignControl chartGlobal Sensitivity AnalysisHybrid Control ChartHybrid design of experimentsHybrid Quality Function DeploymentHybrid Response Surface MethodologyHybrid Six Sigma DMAICHybrid Taguchi MethodIndustrial Applications Response Surface MethodologyLatin Hypercube SamplingMulti-response Design of ExperimentsMulti-response Fractional Factorial DesignMulti-response full factorial designMulti-response Process Capability AnalysisMulti-response Response Surface MethodologyMulti-response Six Sigma DMAICMulti-response Taguchi methodOptimization-assisted design of experimentsOptimization-assisted failure mode and effects analysisOptimization-assisted fractional factorial designOptimization-assisted full factorial designOptimization-assisted process capability analysisOptimization-assisted quality function deploymentOptimization-assisted Reliability AnalysisOptimization-assisted response surface methodologyOptimization-assisted Six Sigma DMAICOptimization-assisted Taguchi methodQuality Function DeploymentRisk-based Box-Behnken DesignRisk-based design of experimentsRisk-based full factorial designRisk-based Taguchi methodRobust Six Sigma DMAICSensitivity Analysis with Control ChartSensitivity Analysis with Process Capability AnalysisSensitivity analysis with root cause analysisSensitivity Analysis with Six Sigma DMAICSensitivity analysis-integrated full factorial designSensitivity analysis-integrated response surface methodologySensitivity Analysis-integrated Taguchi MethodSimulation-assisted design of experimentsSimulation-assisted fractional factorial designSimulation-assisted full factorial designSimulation-assisted process capability analysisSimulation-assisted quality function deploymentSimulation-assisted response surface methodologySimulation-assisted Six Sigma DMAICSimulation-assisted statistical process controlSimulation-assisted Taguchi methodStatistical Process ControlSurrogate-Based Optimization