Compara mètodes

	Enginyeria de la Qualitat Robusta	Anàlisi de Modes i Efectes de Fallada Robusta
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	2000s (robust extensions of QFD originating 1966)	1980s–1990s
Autor original	Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s)	Extension of traditional FMEA (MIL-P-1629, 1949) integrated with Taguchi robust design philosophy (Genichi Taguchi, 1980s)
Tipus	Hybrid quality-engineering planning method	Risk analysis with variability quantification
Font seminal	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.	Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. ISBN: 978-0873895989
Àlies	Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality	Robust FMEA, Noise-Aware FMEA, Variability-Integrated FMEA, Robustness-Based FMEA
Relacionats	4	4
Resum	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 Failure Mode and Effects Analysis extends the classical FMEA framework by explicitly incorporating noise factors, parameter variability, and environmental variation into the risk assessment process. Rather than treating failure likelihood as a single deterministic estimate, it uses robust design principles — most notably from Taguchi's quality engineering — to evaluate how process variability and uncontrollable noise factors influence the probability and severity of each failure mode, yielding risk priority numbers that reflect real-world variability.