Comparar métodos

	Gráfico de Controle Assistido por Simulação	Simulação de Monte Carlo
Área	Delineamento experimental	Tomada de decisão
Família	Process / pipeline	MCDM
Ano de origem	1920s (control charts); simulation integration from 1980s–1990s	1949
Autor original	Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators	Metropolis, N., Ulam, S.
Tipo	Hybrid quality monitoring method	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminal	Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Outros nomes	simulation-based SPC, Monte Carlo control chart design, simulation-enhanced SPC, virtual control chart	—
Relacionados	6	0
Resumo	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.