Comparar métodos

	Despliegue de la Función de Calidad Asistido por Optimización	Metodología de Superficie de Respuesta (RSM)
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Hypothesis test
Año de origen	1990s–2000s (QFD base: ~1966)	1951
Autor original	Yoji Akao (QFD); optimization extensions by various researchers (1990s–2000s)	George E. P. Box & K. B. Wilson
Tipo	Integrated engineering design method	Second-order polynomial response surface model
Fuente seminal	Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA. 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.
Alias	Optimization-integrated QFD, QFD with optimization, Mathematical programming QFD, OA-QFD	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	4	7
Resumen	Optimization-assisted QFD extends the classic House of Quality framework by embedding mathematical optimization — linear programming, multi-objective optimization, or metaheuristics — directly into the QFD process. This allows engineers to simultaneously maximize customer satisfaction and minimize cost or resource constraints when setting target values for engineering characteristics, going beyond the largely subjective priority rankings of traditional QFD.	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.