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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Quality Function Deployment Ibrida× | Quality Function Deployment Robusto× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1966 (QFD foundation); hybrid variants from mid-1990s onward | 2000s (robust extensions of QFD originating 1966) |
| Ideatore≠ | Yoji Akao (QFD foundation); hybrid extensions by various authors integrating fuzzy sets, AHP, TOPSIS, and optimization | Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s) |
| Tipo≠ | Integrated engineering design and decision method | Hybrid quality-engineering planning method |
| Fonte seminale≠ | Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press. ISBN: 978-0915299416 | 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 ↗ |
| Alias | Hybrid QFD, Integrated QFD, QFD hybrid approach, Extended Quality Function Deployment | Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality |
| Correlati | 4 | 4 |
| Sintesi≠ | Hybrid Quality Function Deployment (Hybrid QFD) extends the classic House of Quality framework by embedding additional analytical techniques — such as fuzzy set theory, Analytic Hierarchy Process, TOPSIS, or optimization algorithms — directly into the QFD pipeline. This integration addresses known weaknesses of standard QFD, such as imprecision in customer ratings and subjectivity in relationship matrices, while preserving the method's core strength: systematically translating the voice of the customer into actionable engineering specifications. | 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. |
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