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|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s (robust extensions of QFD originating 1966) | 1989–1990s (formalized in peer-reviewed literature) |
| 提出者≠ | Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s) | Rocke, D. M.; Tatum, L. G. (key contributors) |
| 类型≠ | Hybrid quality-engineering planning method | Robust statistical monitoring framework |
| 开创性文献≠ | Fung, R. Y. K., Tang, J., & Tu, Y. (2002). Modeling of quality function deployment planning under resource allocation constraints. Computers & Industrial Engineering, 43(1–2), 313–328. link ↗ | Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141. DOI ↗ |
| 别名 | Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality | Robust SPC, Resistant SPC, Outlier-robust process monitoring, Robust process surveillance |
| 相关≠ | 4 | 5 |
| 摘要≠ | Robust Quality Function Deployment (Robust QFD) extends the classical House of Quality framework by explicitly modeling uncertainty and variability in customer requirements, perception ratings, and engineering correlation judgments. Instead of treating inputs as crisp single-point values, it applies fuzzy sets, interval analysis, or Taguchi-inspired robustness techniques to ensure that the resulting design targets remain stable and customer-satisfying even when inputs are imprecise or fluctuating. | Robust Statistical Process Control (Robust SPC) is an engineering quality-monitoring framework that replaces the classical mean and standard deviation estimators used in Shewhart-type control charts with outlier-resistant alternatives — such as the median, MAD, or trimmed statistics — so that isolated contaminating observations or non-normal process distributions do not inflate control limits and mask genuine process shifts. |
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