Vertaile menetelmiä

	Risk-perusteinen koejärjestely	Vastauspintamenetelmä (RSM)
Tieteenala	Koesuunnittelu	Koesuunnittelu
Menetelmäperhe	Process / pipeline	Hypothesis test
Syntyvuosi	2000s–2010s (formalized in pharmaceutical and process engineering contexts)	1951
Kehittäjä	Emerged from ICH Q8/Q9/Q10 pharmaceutical guidelines; formalized in engineering by integration of FMEA/FTA with classical DoE	George E. P. Box & K. B. Wilson
Tyyppi	Experimental design method with risk-based factor prioritization	Second-order polynomial response surface model
Alkuperäislähde	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	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Rinnakkaisnimet	Risk-based DoE, risk-informed experimental design, risk-prioritized DoE, RB-DoE	RSM, Central Composite Design, Box-Behnken Design, CCD
Liittyvät	4	7
Tiivistelmä	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.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.