Vertaile menetelmiä

	Simulaatioavusteinen laatutoiminnon käyttöönotto	Robust Quality Function Deployment
Tieteenala	Koesuunnittelu	Koesuunnittelu
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1990s–2000s (QFD: 1966; simulation integration: ~1995–2005)	2000s (robust extensions of QFD originating 1966)
Kehittäjä	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)
Tyyppi	Hybrid engineering design and quality planning method	Hybrid quality-engineering planning method
Alkuperäislähde	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.
Rinnakkaisnimet	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
Liittyvät	6	4
Tiivistelmä	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.