Comparar métodos

	Diseño factorial completo integrado con análisis de sensibilidad	Diseño de Experimentos
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s–2000s (formalized combination)	1935
Autor original	Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)	Ronald A. Fisher
Tipo	Experimental design with factor importance ranking	Experimental planning framework
Fuente seminal	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons. ISBN: 978-0470059975	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	SA-FFD, full factorial design with sensitivity analysis, factorial-based sensitivity analysis, FFD-SA	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	3	3
Resumen	Sensitivity analysis-integrated full factorial design combines exhaustive factorial experimentation — where every combination of factor levels is tested — with systematic sensitivity analysis to quantify how much each input factor drives variation in the output response. This hybrid approach provides both reliable effect estimates and a ranked picture of factor importance, guiding engineers and scientists toward the levers that truly matter for system performance.	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.