Comparar métodos

	Análise de Sensibilidade com Six Sigma DMAIC	Desenho de Experimentos
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	2000s–2010s (applied integration era)	1935
Autor original	Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s)	Ronald A. Fisher
Tipo	Hybrid process-improvement and uncertainty-quantification pipeline	Experimental planning framework
Fonte seminal	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.
Outros nomes	SA-DMAIC, DMAIC sensitivity analysis, sensitivity-informed Six Sigma, Six Sigma sensitivity integration	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	5	3
Resumo	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.