Comparer des méthodes

	Analyse de Sensibilité et Analyse de Capabilité des Processus	Simulation de Monte-Carlo
Domaine	Plans d'expériences	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1986–2000s (Cp/Cpk indices from Kane 1986; integration formalized in Six Sigma era)	1949
Auteur d'origine	Synthesized from work by V. E. Kane (process capability indices) and A. Saltelli (sensitivity analysis); integrated in Six Sigma and quality engineering practice	Metropolis, N., Ulam, S.
Type	Quantitative engineering analysis	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	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	Sensitivity-Capability Analysis, PCA with Sensitivity Analysis, Process Capability Sensitivity Study, Cp/Cpk Sensitivity Analysis	—
Apparentées	5	0
Résumé	Sensitivity analysis with process capability analysis is a quantitative engineering method that combines the measurement of process performance — via capability indices such as Cp and Cpk — with systematic variation of input factors to identify which factors most strongly influence whether a process meets its specification limits. It is widely used in Six Sigma projects, manufacturing quality improvement, and Design of Experiments contexts to prioritize where corrective action will yield the greatest gain in process capability.	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.