Comparer des méthodes

	Analyse de sensibilité avec analyse des causes profondes	Simulation de Monte-Carlo
Domaine	Plans d'expériences	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1990s–2000s (formalized integration in reliability and quality engineering literature)	1949
Auteur d'origine	Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe)	Metropolis, N., Ulam, S.
Type	Integrated diagnostic and optimization method	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	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
Alias	SA-RCA, sensitivity-driven root cause analysis, parameter sensitivity with failure analysis, sensitivity-informed RCA	—
Apparentées	4	0
Résumé	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.