방법 비교

	시뮬레이션 지원 식스 시그마 DMAIC	강건한 Six Sigma DMAIC
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	2000s–present (systematic integration of simulation with DMAIC)	1990s–2000s (integration period)
창시자	Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)	Motorola (Six Sigma, 1986); Taguchi robust design integrated into DMAIC by quality engineering practitioners in the 1990s–2000s
유형	Hybrid process-improvement methodology	Hybrid process improvement and robust engineering methodology
원전	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons. ISBN: 978-0470169926	Antony, J. (2006). Six Sigma for service processes. Business Process Management Journal, 12(2), 234–248.
별칭	Sim-DMAIC, Simulation-integrated DMAIC, Six Sigma with simulation, DMAIC simulation modeling	Robust DMAIC, Six Sigma with Robust Design, Taguchi-integrated DMAIC, R-DMAIC
관련	6	4
요약	Simulation-assisted Six Sigma DMAIC embeds discrete-event or Monte Carlo simulation models inside the classic DMAIC cycle (Define, Measure, Analyze, Improve, Control) to test process changes virtually before committing to physical implementation. By running thousands of simulated scenarios, teams quantify variation, identify bottlenecks, and verify improvement hypotheses at low cost and with minimal disruption to live operations.	Robust Six Sigma DMAIC embeds Taguchi's robust design philosophy within the classic Define-Measure-Analyze-Improve-Control framework. Rather than optimizing a process only for average performance, this hybrid approach simultaneously minimizes process variation caused by noise factors — environmental shifts, material lot differences, operator variability — so that the outcome remains near target even when uncontrollable conditions change. The result is a process that is both capable and insensitive to real-world disturbances.