Compară metode

	Analiza de Sensibilitate Integrată cu Designul Factorial Complet	Metodologia Suprafeței de Răspuns (RSM)
Domeniu	Design experimental	Design experimental
Familie	Process / pipeline	Hypothesis test
Anul apariției	1990s–2000s (formalized combination)	1951
Autorul original	Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)	George E. P. Box & K. B. Wilson
Tip	Experimental design with factor importance ranking	Second-order polynomial response surface model
Sursa seminală	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	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.
Denumiri alternative	SA-FFD, full factorial design with sensitivity analysis, factorial-based sensitivity analysis, FFD-SA	RSM, Central Composite Design, Box-Behnken Design, CCD
Înrudite	3	7
Rezumat	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.	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.