Methoden vergleichen

	Multi-response Six Sigma DMAIC	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s–2010s (applied integration era)	1935
Urheber	Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community	Ronald A. Fisher
Typ	Process improvement methodology with multi-objective optimization	Experimental planning framework
Wegweisende Quelle	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.
Aliasnamen	MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	5	3
Zusammenfassung	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.