Comparer des méthodes

	Conception factorielle complète intégrée à l'analyse de sensibilité	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (formalized combination)	1935
Auteur d'origine	Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)	Ronald A. Fisher
Type	Experimental design with factor importance ranking	Experimental planning framework
Source fondatrice	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
Apparentées	3	3
Résumé	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.