পদ্ধতির তুলনা করুন

	সিমুলেশন-সহায়ক পরিসংখ্যানগত প্রক্রিয়া নিয়ন্ত্রণ	পরিসংখ্যানিক প্রক্রিয়া নিয়ন্ত্রণ
ক্ষেত্র	পরীক্ষামূলক নকশা	পরীক্ষামূলক নকশা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1980s–present	1924–1931
প্রবর্তক	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Walter A. Shewhart
ধরন	Hybrid quantitative method	Process monitoring and quality control method
মৌলিক উৎস	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
অপর নাম	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	SPC, statistical quality control, process control charting, Shewhart control
সম্পর্কিত	6	6
সারসংক্ষেপ	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.	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.