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| Многокритериален Шест Сигми DMAIC× | Статистически контрол на процеси× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2000s–2010s (applied integration era) | 1924–1931 |
| Създател≠ | Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community | Walter A. Shewhart |
| Тип≠ | Process improvement methodology with multi-objective optimization | Process monitoring and quality control method |
| Основополагащ източник≠ | Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday. ISBN: 978-0385494090 | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762 |
| Други названия | MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC | SPC, statistical quality control, process control charting, Shewhart control |
| Свързани≠ | 5 | 6 |
| Резюме≠ | 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. | Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers. |
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