Comparer des méthodes

	Déploiement de la fonction qualité basé sur les risques	Déploiement Robuste de la Qualité (Robust Quality Function Deployment)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (QFD: 1966–1972; risk-based extensions: ~1995–2010)	2000s (robust extensions of QFD originating 1966)
Auteur d'origine	Yoji Akao (QFD foundation); risk integration developed by multiple authors in quality engineering literature from the 1990s onward	Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s)
Type	Structured quality planning method with integrated risk assessment	Hybrid quality-engineering planning method
Source fondatrice	Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA. 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.
Alias	Risk-based QFD, QFD with risk analysis, FMEA-integrated QFD, risk-integrated House of Quality	Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality
Apparentées	6	4
Résumé	Risk-based quality function deployment (Risk-based QFD) integrates formal risk analysis — most commonly Failure Mode and Effects Analysis (FMEA) or risk matrices — into the classic QFD House of Quality framework. By weighting customer requirements and engineering characteristics against their associated failure risks, teams prioritise design and process decisions not only by customer importance but also by potential harm, regulatory exposure, or reliability impact. It is widely used in automotive, aerospace, medical device, and industrial product development.	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.