Methoden vergleichen

	Sensitivitätsanalyse mit Six Sigma DMAIC	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s–2010s (applied integration era)	1935
Urheber	Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s)	Ronald A. Fisher
Typ	Hybrid process-improvement and uncertainty-quantification pipeline	Experimental planning framework
Wegweisende Quelle	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Aliasnamen	SA-DMAIC, DMAIC sensitivity analysis, sensitivity-informed Six Sigma, Six Sigma sensitivity integration	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	5	3
Zusammenfassung	Sensitivity analysis integrated with Six Sigma DMAIC augments the classic Define-Measure-Analyze-Improve-Control cycle with formal quantification of how much each input variable contributes to output variation. By embedding local or global sensitivity indices inside the Analyze phase, practitioners move beyond correlation screening to rigorously rank which process factors drive defect rates, guiding improvement resources to where they matter most.	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.