Compară metode

	DMAIC Asistat de Simulare Six Sigma	Control statistic asistat de simulare
Domeniu	Design experimental	Design experimental
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s–present (systematic integration of simulation with DMAIC)	1980s–present
Autorul original	Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward
Tip	Hybrid process-improvement methodology	Hybrid quantitative method
Sursa seminală	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons. ISBN: 978-0470169926	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926
Denumiri alternative	Sim-DMAIC, Simulation-integrated DMAIC, Six Sigma with simulation, DMAIC simulation modeling	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC
Înrudite	6	6
Rezumat	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.	Simulation-assisted statistical process control (SA-SPC) combines computer simulation — typically Monte Carlo or discrete-event simulation — with classical SPC methods to design, test, and calibrate control charts and monitoring schemes before or alongside deployment on a real production process. Rather than relying solely on closed-form analytical assumptions, SA-SPC uses simulated data to evaluate chart performance under realistic, often non-normal process conditions.