Сравнение методов

	Статистический контроль процессов с помощью имитационного моделирования	Shewhart Charts
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1980s–present	1924 (first use); 1931 (seminal book)
Автор метода	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Walter A. Shewhart (Bell Labs)
Тип	Hybrid quantitative method	Statistical monitoring and control technique
Основополагающий источник	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.
Другие названия	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Связанные	6	6
Сводка	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.	A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.