Сравнение методов

	Проектирование экспериментов на основе оценки рисков	Планирование эксперимента
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s–2010s (formalized in pharmaceutical and process engineering contexts)	1935
Автор метода	Emerged from ICH Q8/Q9/Q10 pharmaceutical guidelines; formalized in engineering by integration of FMEA/FTA with classical DoE	Ronald A. Fisher
Тип	Experimental design method with risk-based factor prioritization	Experimental planning framework
Основополагающий источник	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916018	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Другие названия	Risk-based DoE, risk-informed experimental design, risk-prioritized DoE, RB-DoE	DOE, experimental design, factorial experimentation, planned experimentation
Связанные	4	3
Сводка	Risk-based design of experiments (RB-DoE) integrates formal risk assessment — typically using tools such as FMEA or fault tree analysis — with classical experimental design to prioritize which process or product factors are most critical to investigate. Rather than treating all candidate factors equally, this approach ranks factors by their risk priority number or likelihood of affecting quality, safety, or reliability, then allocates experimental runs preferentially to high-risk factors. It is widely used in pharmaceutical development, chemical process engineering, and manufacturing quality management.	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.