Methoden vergleichen

	Optimierungsgestützte Six Sigma DMAIC	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s (integration period)	1935
Urheber	Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s	Ronald A. Fisher
Typ	Process improvement framework with embedded optimization	Experimental planning framework
Wegweisende Quelle	Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Aliasnamen	Optimization-integrated DMAIC, DMAIC with optimization, Six Sigma optimization framework, Opt-DMAIC	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	5	3
Zusammenfassung	Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.	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.