Comparar métodos

	Análise de Sensibilidade com Análise de Causa Raiz	Simulação de Monte Carlo
Área	Delineamento experimental	Tomada de decisão
Família	Process / pipeline	MCDM
Ano de origem	1990s–2000s (formalized integration in reliability and quality engineering literature)	1949
Autor original	Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe)	Metropolis, N., Ulam, S.
Tipo	Integrated diagnostic and optimization method	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminal	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons. ISBN: 978-0470059975	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Outros nomes	SA-RCA, sensitivity-driven root cause analysis, parameter sensitivity with failure analysis, sensitivity-informed RCA	—
Relacionados	4	0
Resumo	Sensitivity Analysis with Root Cause Analysis (SA-RCA) is an integrated engineering method that first quantifies how much each input parameter or process variable drives variability in a system output, then applies structured root cause analysis to the most influential factors to identify and eliminate the underlying failure mechanisms. The combination transforms numerical rankings of influence into actionable diagnoses, making it particularly effective in quality engineering, reliability analysis, and process improvement contexts.	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.