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	Quality Function Deployment assistita da simulazione	Progettazione di Esperimenti Assistita da Simulazione
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1990s–2000s (QFD: 1966; simulation integration: ~1995–2005)	1970s–1990s (formalized with computer experimentation growth)
Ideatore	Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.
Tipo	Hybrid engineering design and quality planning method	Hybrid experimental-computational method
Fonte seminale	Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press. ISBN: 978-0915299416	Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202
Alias	SA-QFD, simulation-integrated QFD, simulation-driven house of quality, QFD with simulation	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE
Correlati	6	5
Sintesi	Simulation-assisted quality function deployment (SA-QFD) integrates computational simulation into the classic QFD framework to replace or supplement costly physical prototypes when evaluating how engineering design decisions satisfy customer requirements. By embedding simulation models — such as finite element analysis, discrete-event simulation, or system dynamics — within the House of Quality matrix, engineers can rapidly quantify the impact of technical characteristics on customer satisfaction and iteratively refine design priorities before committing to production.	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.