Comparar métodos

	Gráfico de control asistido por simulación	Control Estadístico de Procesos
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1920s (control charts); simulation integration from 1980s–1990s	1924–1931
Autor original	Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators	Walter A. Shewhart
Tipo	Hybrid quality monitoring method	Process monitoring and quality control method
Fuente seminal	Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	simulation-based SPC, Monte Carlo control chart design, simulation-enhanced SPC, virtual control chart	SPC, statistical quality control, process control charting, Shewhart control
Relacionados	6	6
Resumen	Simulation-assisted control chart integrates Monte Carlo or discrete-event simulation with traditional Shewhart-type control charting to design, validate, and optimize chart parameters before deployment on a real process. Rather than relying solely on assumed distributional forms, the practitioner builds a simulation model of the process, generates virtual data under in-control and out-of-control scenarios, and uses these runs to calibrate control limits, estimate average run length (ARL), and stress-test chart sensitivity — all without interrupting production.	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.