手法を比較

	Multi-response Six Sigma DMAIC	多応答応答曲面法
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	2000s–2010s (applied integration era)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
提唱者	Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
種類	Process improvement methodology with multi-objective optimization	Experimental optimization technique
原典	Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday. ISBN: 978-0385494090	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
別名	MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
関連	5	6
概要	Multi-response Six Sigma DMAIC extends the classic Define-Measure-Analyze-Improve-Control framework to situations where a process must satisfy several quality characteristics simultaneously. Rather than optimizing a single output, the methodology integrates multi-response optimization techniques — such as desirability functions, TOPSIS, or weighted signal-to-noise ratios — within the Analyze and Improve phases to identify factor settings that jointly meet all quality targets.	Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.