Confronta i metodi

	Progettazione Fattoriale Completa Multi-Risposta	Metodologia delle Superfici di Risposta (RSM)
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Hypothesis test
Anno di origine	1950s–1980s	1951
Ideatore	Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)	George E. P. Box & K. B. Wilson
Tipo	Experimental design with multi-objective optimization	Second-order polynomial response surface model
Fonte seminale	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	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	MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design	RSM, Central Composite Design, Box-Behnken Design, CCD
Correlati	3	7
Sintesi	Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously.	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.