Comparer des méthodes

	Analyse de sensibilité - Plan d'expériences intégré	Méthodologie des surfaces de réponse (RSM)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1990s–2000s (formal integration emerged in simulation and engineering optimization literature)	1951
Auteur d'origine	Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator	George E. P. Box & K. B. Wilson
Type	Hybrid experimental-analytical framework	Second-order polynomial response surface model
Source fondatrice	Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley. ISBN: 9780470870938	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	SA-DoE, SA-integrated DoE, DoE with sensitivity screening, factor screening with sensitivity analysis	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	3	7
Résumé	Sensitivity Analysis-Integrated Design of Experiments (SA-DoE) combines systematic experimental planning with formal sensitivity analysis to identify which input factors most strongly influence a response, then efficiently characterises those factors' effects. By embedding sensitivity screening into the DoE workflow, experimenters avoid wasting trials on inert variables and focus resources on the factors that truly drive system behaviour — making it especially valuable in simulation studies, product engineering, and complex process optimisation.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.