Comparer des méthodes

	Six Sigma DMAIC multi-réponse	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s (applied integration era)	1935
Auteur d'origine	Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community	Ronald A. Fisher
Type	Process improvement methodology with multi-objective optimization	Experimental planning framework
Source fondatrice	Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday. ISBN: 978-0385494090	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	5	3
Résumé	Multi-response Six Sigma DMAIC extends the classic Define-Measure-Analyze-Improve-Control framework to situations where a process must satisfy several quality characteristics simultaneously. Rather than optimizing a single output, the methodology integrates multi-response optimization techniques — such as desirability functions, TOPSIS, or weighted signal-to-noise ratios — within the Analyze and Improve phases to identify factor settings that jointly meet all quality targets.	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.