Сравнение на методи

	Статистически контрол на процесите с помощта на симулация	Статистически контрол на процеси
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1980s–present	1924–1931
Създател	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Walter A. Shewhart
Тип	Hybrid quantitative method	Process monitoring and quality control method
Основополагащ източник	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
Други названия	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	SPC, statistical quality control, process control charting, Shewhart control
Свързани	6	6
Резюме	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.