Comparer des méthodes

	Analyse de Capabilité Assistée par Simulation	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–1990s (mature practice by mid-1990s)	1935
Auteur d'origine	Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)	Ronald A. Fisher
Type	Quantitative engineering quality method	Experimental planning framework
Source fondatrice	Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold. ISBN: 978-0340691281	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	6	3
Résumé	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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.