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| Analiza wrażliwości z analizą przyczyn źródłowych× | Projektowanie Doświadczeń× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1990s–2000s (formalized integration in reliability and quality engineering literature) | 1935 |
| Twórca≠ | Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe) | Ronald A. Fisher |
| Typ≠ | Integrated diagnostic and optimization method | Experimental planning framework |
| Źródło pierwotne≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons. ISBN: 978-0470059975 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Inne nazwy | SA-RCA, sensitivity-driven root cause analysis, parameter sensitivity with failure analysis, sensitivity-informed RCA | DOE, experimental design, factorial experimentation, planned experimentation |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Sensitivity Analysis with Root Cause Analysis (SA-RCA) is an integrated engineering method that first quantifies how much each input parameter or process variable drives variability in a system output, then applies structured root cause analysis to the most influential factors to identify and eliminate the underlying failure mechanisms. The combination transforms numerical rankings of influence into actionable diagnoses, making it particularly effective in quality engineering, reliability analysis, and process improvement contexts. | 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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