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| Симулационно-подпомогнат Six Sigma DMAIC× | Статистически контрол на процесите с помощта на симулация× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2000s–present (systematic integration of simulation with DMAIC) | 1980s–present |
| Създател≠ | 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 |
| Тип≠ | Hybrid process-improvement methodology | Hybrid quantitative method |
| Основополагащ източник≠ | 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 |
| Други названия | Sim-DMAIC, Simulation-integrated DMAIC, Six Sigma with simulation, DMAIC simulation modeling | Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC |
| Свързани | 6 | 6 |
| Резюме≠ | 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. |
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