Methoden vergleichen

	Simulationsgestützte Prozessfähigkeitsanalyse	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–1990s (mature practice by mid-1990s)	1935
Urheber	Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)	Ronald A. Fisher
Typ	Quantitative engineering quality method	Experimental planning framework
Wegweisende Quelle	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.
Aliasnamen	Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	6	3
Zusammenfassung	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.