Methoden vergleichen

	Optimierungsgestützte Prozessfähigkeitsanalyse	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1986–2000s	1935
Urheber	V. E. Kane (capability indices, 1986); integrated with optimization frameworks by quality engineering researchers in the 1990s–2000s	Ronald A. Fisher
Typ	Quantitative engineering method	Experimental planning framework
Wegweisende Quelle	Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Aliasnamen	OA-PCA, optimization-integrated capability analysis, capability-constrained process optimization, process capability with optimization	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	5	3
Zusammenfassung	Optimization-assisted process capability analysis combines classical capability indices (Cp, Cpk, Cpm) with mathematical optimization to identify process parameter settings that simultaneously satisfy engineering specifications and maximize process capability. Rather than simply measuring whether a process is capable, it prescribes the control factor levels — mean, variance, tolerances — that push capability above a target threshold. It is widely applied in manufacturing, chemical processing, and quality engineering contexts where multiple process variables must be tuned jointly.	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.