Linganisha mbinu

	Uchanganuzi wa Uwezo wa Mchakato kwa Msaada wa Simulizi	Uiguzi wa Monte Carlo
Nyanja	Muundo wa Majaribio	Ufanyaji Maamuzi
Familia	Process / pipeline	MCDM
Mwaka wa asili	1980s–1990s (mature practice by mid-1990s)	1949
Mwanzilishi	Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)	Metropolis, N., Ulam, S.
Aina	Quantitative engineering quality method	Robustness wrapper — Monte Carlo uncertainty propagation
Chanzo asilia	Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold. ISBN: 978-0340691281	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Majina mbadala	Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study	—
Zinazohusiana	6	0
Muhtasari	Simulation-assisted process capability analysis combines Monte Carlo simulation with classical capability indices (Cp, Cpk, Cpm) to evaluate whether a process can consistently meet specification limits when direct measurement is costly, dangerous, or impractical. By propagating input distributions through a process model, the analyst obtains a simulated output distribution and derives capability metrics without waiting for physical production runs. The approach is especially valuable during product design, process scale-up, and tolerance stack-up studies.	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.