পদ্ধতির তুলনা করুন

	সিমুলেশন-সহায়তায় সিক্স সিগমা DMAIC	পরিসংখ্যানিক প্রক্রিয়া নিয়ন্ত্রণ
ক্ষেত্র	পরীক্ষামূলক নকশা	পরীক্ষামূলক নকশা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2000s–present (systematic integration of simulation with DMAIC)	1924–1931
প্রবর্তক	Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)	Walter A. Shewhart
ধরন	Hybrid process-improvement methodology	Process monitoring and quality control method
মৌলিক উৎস	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons. ISBN: 978-0470169926	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
অপর নাম	Sim-DMAIC, Simulation-integrated DMAIC, Six Sigma with simulation, DMAIC simulation modeling	SPC, statistical quality control, process control charting, Shewhart control
সম্পর্কিত	6	6
সারসংক্ষেপ	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.	Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.