Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Six Sigma DMAIC multi-réponse× | Méthodologie des surfaces de réponse à réponses multiples× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2000s–2010s (applied integration era) | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Auteur d'origine≠ | Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Type≠ | Process improvement methodology with multi-objective optimization | Experimental optimization technique |
| Source fondatrice≠ | Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday. ISBN: 978-0385494090 | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Alias | MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Apparentées≠ | 5 | 6 |
| Résumé≠ | Multi-response Six Sigma DMAIC extends the classic Define-Measure-Analyze-Improve-Control framework to situations where a process must satisfy several quality characteristics simultaneously. Rather than optimizing a single output, the methodology integrates multi-response optimization techniques — such as desirability functions, TOPSIS, or weighted signal-to-noise ratios — within the Analyze and Improve phases to identify factor settings that jointly meet all quality targets. | Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once. |
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