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| Simulationsgestütztes Quality Function Deployment× | Robuste Qualitätsfunktionsbereitstellung× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990s–2000s (QFD: 1966; simulation integration: ~1995–2005) | 2000s (robust extensions of QFD originating 1966) |
| Urheber≠ | Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s | Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s) |
| Typ≠ | Hybrid engineering design and quality planning method | Hybrid quality-engineering planning method |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | SA-QFD, simulation-integrated QFD, simulation-driven house of quality, QFD with simulation | Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | Simulation-assisted quality function deployment (SA-QFD) integrates computational simulation into the classic QFD framework to replace or supplement costly physical prototypes when evaluating how engineering design decisions satisfy customer requirements. By embedding simulation models — such as finite element analysis, discrete-event simulation, or system dynamics — within the House of Quality matrix, engineers can rapidly quantify the impact of technical characteristics on customer satisfaction and iteratively refine design priorities before committing to production. | 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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