Сравнение методов

	Risk-Based Full Factorial Design	Планирование эксперимента
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s (formal integration with risk frameworks circa 2005–2009)	1935
Автор метода	Developed at the intersection of classical factorial experimentation (Fisher, 1935) and formal risk analysis frameworks (ICH Q8/Q9, 2005–2009)	Ronald A. Fisher
Тип	Structured experimental design with risk-informed factor prioritization	Experimental planning framework
Основополагающий источник	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Другие названия	risk-informed full factorial design, RB-FFD, risk-prioritized factorial experiment, risk-based FFD	DOE, experimental design, factorial experimentation, planned experimentation
Связанные	3	3
Сводка	Risk-based full factorial design integrates formal risk analysis — typically Failure Mode and Effects Analysis (FMEA) or a comparable risk-ranking tool — with a full factorial experiment to ensure that factors posing the greatest quality or safety risk receive exhaustive experimental coverage. All combinations of selected factor levels are run, but the selection of which factors to include and the range of their levels is explicitly guided by prior risk scores rather than purely by engineering intuition or resource availability.	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.