Salīdzināt metodes

	Simulācijas atbalstīta Six Sigma DMAIC	Statistiskās procesa kontroles (SPC) simulācijas palīglīdzeklis
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–present (systematic integration of simulation with DMAIC)	1980s–present
Autors	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
Tips	Hybrid process-improvement methodology	Hybrid quantitative method
Pirmavots	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
Citi nosaukumi	Sim-DMAIC, Simulation-integrated DMAIC, Six Sigma with simulation, DMAIC simulation modeling	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC
Saistītās	6	6
Kopsavilkums	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.