Compara mètodes

	Desplegament de la Funció Qualitat assistit per simulació	Metodologia de Superfície de Resposta (RSM)
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Hypothesis test
Any d'origen	1990s–2000s (QFD: 1966; simulation integration: ~1995–2005)	1951
Autor original	Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s	George E. P. Box & K. B. Wilson
Tipus	Hybrid engineering design and quality planning method	Second-order polynomial response surface model
Font seminal	Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press. ISBN: 978-0915299416	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.
Àlies	SA-QFD, simulation-integrated QFD, simulation-driven house of quality, QFD with simulation	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionats	6	7
Resum	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.	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.