השוואת שיטות

	תכנון Box-Behnken מבוסס-סיכונים	תכנון מרכזי מורכב – תכנון ניסויי של משטח תגובה
תחום	תכנון ניסויים	תכנון ניסויים
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2005–2009 (QbD-era integration of risk assessment with BBD)	1951
הוגה השיטה	Box & Behnken (BBD, 1960); risk integration formalized under ICH Q8/Q9 pharmaceutical QbD frameworks (~2005–2009)	George E. P. Box and K. B. Wilson
סוג	Response surface experimental design with risk prioritization	Response surface experimental design
מקור מכונן	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.
כינויים	Risk-based BBD, Risk-prioritized Box-Behnken, QbD Box-Behnken design, Risk-informed RSM	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
קשורות	4	3
תקציר	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.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.