Sammenlign metoder

	Simuleringsassistert Six Sigma DMAIC	Optimalisering-assistert Six Sigma DMAIC
Fagfelt	Forsøksdesign	Forsøksdesign
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2000s–present (systematic integration of simulation with DMAIC)	1990s–2000s (integration period)
Opphavsperson	Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)	Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s
Type	Hybrid process-improvement methodology	Process improvement framework with embedded optimization
Opprinnelig kilde	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons. ISBN: 978-0470169926	Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27.
Alias	Sim-DMAIC, Simulation-integrated DMAIC, Six Sigma with simulation, DMAIC simulation modeling	Optimization-integrated DMAIC, DMAIC with optimization, Six Sigma optimization framework, Opt-DMAIC
Relaterte	6	5
Sammendrag	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.	Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.