Comparar métodos

	Delineamento Box-Behnken Baseado em Risco	Metodologia de Superfície de Resposta (RSM)
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Hypothesis test
Ano de origem	2005–2009 (QbD-era integration of risk assessment with BBD)	1951
Autor original	Box & Behnken (BBD, 1960); risk integration formalized under ICH Q8/Q9 pharmaceutical QbD frameworks (~2005–2009)	George E. P. Box & K. B. Wilson
Tipo	Response surface experimental design with risk prioritization	Second-order polynomial response surface model
Fonte seminal	Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.	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.
Outros nomes	Risk-based BBD, Risk-prioritized Box-Behnken, QbD Box-Behnken design, Risk-informed RSM	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	4	7
Resumo	Risk-based Box-Behnken Design combines the classical three-level Box-Behnken response surface design with a formal risk assessment step — typically a risk ranking tool such as FMEA or Ishikawa analysis — to prioritize which process or formulation factors deserve experimental investigation. Widely adopted in pharmaceutical Quality by Design (QbD) and engineering process optimization, the approach ensures that experimental resources are directed toward the factor combinations most likely to affect product quality or process performance, reducing unnecessary runs while preserving predictive power.	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.