Confronta i metodi

	Controllo Statistico di Processo Assistito da Simulazione	Simulazione Monte Carlo
Campo	Disegno sperimentale	Processo decisionale
Famiglia	Process / pipeline	MCDM
Anno di origine	1980s–present	1949
Ideatore	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Metropolis, N., Ulam, S.
Tipo	Hybrid quantitative method	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminale	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	—
Correlati	6	0
Sintesi	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.	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.