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	Progettazione fattoriale frazionaria assistita da simulazione	Progettazione di Esperimenti Assistita da Simulazione
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	FFD: 1950s; simulation integration: 1980s–2000s	1970s–1990s (formalized with computer experimentation growth)
Ideatore	Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration)	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.
Tipo	Experimental design with computational augmentation	Hybrid experimental-computational method
Fonte seminale	Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer. ISBN: 978-0387718125	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202
Alias	SA-FFD, virtual fractional factorial design, computer-aided fractional factorial design, simulation-based FFD	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE
Correlati	4	5
Sintesi	Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level combinations — the fractional factorial array — is executed inside a validated simulation model instead of (or alongside) a real process, dramatically reducing resource requirements while preserving the ability to identify main effects and low-order interactions.	Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or hazardous physical trials, researchers execute a structured set of virtual experiments across selected factor combinations, then fit a surrogate model to the simulation outputs to understand main effects, interactions, and optimal settings.