Methoden vergleichen

	Multi-Response Full Factorial Design	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1950s–1980s	1935
Urheber	Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)	Ronald A. Fisher
Typ	Experimental design with multi-objective optimization	Experimental planning framework
Wegweisende Quelle	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Aliasnamen	MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	3	3
Zusammenfassung	Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics 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.