Methoden vergleichen

	Sensitivitätsanalyse mit Ursachenanalyse	Monte-Carlo-Simulation
Fachgebiet	Versuchsplanung	Entscheidungsfindung
Familie	Process / pipeline	MCDM
Entstehungsjahr	1990s–2000s (formalized integration in reliability and quality engineering literature)	1949
Urheber	Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe)	Metropolis, N., Ulam, S.
Typ	Integrated diagnostic and optimization method	Robustness wrapper — Monte Carlo uncertainty propagation
Wegweisende Quelle	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
Aliasnamen	SA-RCA, sensitivity-driven root cause analysis, parameter sensitivity with failure analysis, sensitivity-informed RCA	—
Verwandt	4	0
Zusammenfassung	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.