Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Sensibilidad con Six Sigma DMAIC× | Six Sigma Robusto DMAIC× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2000s–2010s (applied integration era) | 1990s–2000s (integration period) |
| Autor original≠ | Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s) | Motorola (Six Sigma, 1986); Taguchi robust design integrated into DMAIC by quality engineering practitioners in the 1990s–2000s |
| Tipo≠ | Hybrid process-improvement and uncertainty-quantification pipeline | Hybrid process improvement and robust engineering methodology |
| Fuente seminal≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975 | Antony, J. (2006). Six Sigma for service processes. Business Process Management Journal, 12(2), 234–248. DOI ↗ |
| Alias | SA-DMAIC, DMAIC sensitivity analysis, sensitivity-informed Six Sigma, Six Sigma sensitivity integration | Robust DMAIC, Six Sigma with Robust Design, Taguchi-integrated DMAIC, R-DMAIC |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | Sensitivity analysis integrated with Six Sigma DMAIC augments the classic Define-Measure-Analyze-Improve-Control cycle with formal quantification of how much each input variable contributes to output variation. By embedding local or global sensitivity indices inside the Analyze phase, practitioners move beyond correlation screening to rigorously rank which process factors drive defect rates, guiding improvement resources to where they matter most. | Robust Six Sigma DMAIC embeds Taguchi's robust design philosophy within the classic Define-Measure-Analyze-Improve-Control framework. Rather than optimizing a process only for average performance, this hybrid approach simultaneously minimizes process variation caused by noise factors — environmental shifts, material lot differences, operator variability — so that the outcome remains near target even when uncontrollable conditions change. The result is a process that is both capable and insensitive to real-world disturbances. |
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