手法を比較

	感度分析統合応答曲面法	応答曲面法 (RSM)
分野	実験計画法	実験計画法
系統	Process / pipeline	Hypothesis test
提唱年	1990s–2000s (integration practice)	1951
提唱者	Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s)	George E. P. Box & K. B. Wilson
種類	Hybrid experimental-analytical method	Second-order polynomial response surface model
原典	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916018	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.
別名	SA-RSM, RSM with sensitivity analysis, sensitivity-augmented RSM, response surface methodology with factor screening	RSM, Central Composite Design, Box-Behnken Design, CCD
関連	5	7
概要	Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum.	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.