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	Bayesian Quality Function Deployment	Jakościowe Rozmieszczenie Wymagań (Robust Quality Function Deployment)
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	QFD: 1966–1972; Bayesian QFD extensions: 2000s–present	2000s (robust extensions of QFD originating 1966)
Twórca	Yoji Akao (QFD); Bayesian extension developed by multiple researchers including Fung, Tang, and colleagues	Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s)
Typ	Probabilistic customer-driven design planning method	Hybrid quality-engineering planning method
Źródło pierwotne	Tang, J., Fung, R. Y. K., Xu, B., & Wang, D. (2002). A new approach to quality function deployment planning with financial consideration. Computers & Operations Research, 29(11), 1447–1463.	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.
Inne nazwy	Bayesian QFD, Probabilistic QFD, Bayesian House of Quality, Bayesian Voice of the Customer Analysis	Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality
Pokrewne	5	4
Podsumowanie	Bayesian Quality Function Deployment (Bayesian QFD) integrates Bayesian probabilistic inference into the classical House of Quality framework to handle uncertainty in customer preference data and relationship matrices. By expressing relationship weights and importance ratings as probability distributions rather than point estimates, it propagates uncertainty through the planning process and yields more defensible engineering prioritization decisions under incomplete or conflicting customer information.	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.