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| Risikobaseret forsøgsdesign× | Design of Experiments× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2000s–2010s (formalized in pharmaceutical and process engineering contexts) | 1935 |
| Ophavsperson≠ | Emerged from ICH Q8/Q9/Q10 pharmaceutical guidelines; formalized in engineering by integration of FMEA/FTA with classical DoE | Ronald A. Fisher |
| Type≠ | Experimental design method with risk-based factor prioritization | Experimental planning framework |
| Oprindelig kilde≠ | 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. link ↗ |
| Aliasser | Risk-based DoE, risk-informed experimental design, risk-prioritized DoE, RB-DoE | DOE, experimental design, factorial experimentation, planned experimentation |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | 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. |
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