Сравнение методов

	Статистический контроль процессов на основе оценки рисков	Статистическое управление процессами
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1920s (SPC foundations); risk-based integration formalized in 2000s–2010s	1924–1931
Автор метода	Integrated from SPC (Shewhart, 1920s; Deming, 1950s) and risk analysis frameworks (FDA ICH Q10, ISO 31000)	Walter A. Shewhart
Тип	Hybrid quality-risk engineering method	Process monitoring and quality control method
Основополагающий источник	Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley. ISBN: 978-1119399308	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Другие названия	Risk-based SPC, RBSPC, risk-prioritized SPC, risk-informed process monitoring	SPC, statistical quality control, process control charting, Shewhart control
Связанные	6	6
Сводка	Risk-based statistical process control (Risk-based SPC) is an engineering quality method that integrates formal risk analysis — typically FMEA or a risk matrix — with statistical process monitoring to focus control chart resources on the process parameters that pose the greatest risk to product quality or system safety. Rather than applying control charts uniformly across all variables, risk-based SPC directs tighter monitoring toward high-risk, high-impact process characteristics identified through structured hazard prioritization.	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.