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| Optimointiavusteinen Six Sigma DMAIC× | Robust Six Sigma DMAIC× | |
|---|---|---|
| Tieteenala | Koesuunnittelu | Koesuunnittelu |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi | 1990s–2000s (integration period) | 1990s–2000s (integration period) |
| Kehittäjä≠ | Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s | Motorola (Six Sigma, 1986); Taguchi robust design integrated into DMAIC by quality engineering practitioners in the 1990s–2000s |
| Tyyppi≠ | Process improvement framework with embedded optimization | Hybrid process improvement and robust engineering methodology |
| Alkuperäislähde≠ | Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27. link ↗ | Antony, J. (2006). Six Sigma for service processes. Business Process Management Journal, 12(2), 234–248. DOI ↗ |
| Rinnakkaisnimet | Optimization-integrated DMAIC, DMAIC with optimization, Six Sigma optimization framework, Opt-DMAIC | Robust DMAIC, Six Sigma with Robust Design, Taguchi-integrated DMAIC, R-DMAIC |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously. | 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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