Сравнение методов

	Робастное развертывание функций качества	Робастное статистическое управление процессами
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s (robust extensions of QFD originating 1966)	1989–1990s (formalized in peer-reviewed literature)
Автор метода	Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s)	Rocke, D. M.; Tatum, L. G. (key contributors)
Тип	Hybrid quality-engineering planning method	Robust statistical monitoring framework
Основополагающий источник	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.	Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141.
Другие названия	Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality	Robust SPC, Resistant SPC, Outlier-robust process monitoring, Robust process surveillance
Связанные	4	5
Сводка	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 Statistical Process Control (Robust SPC) is an engineering quality-monitoring framework that replaces the classical mean and standard deviation estimators used in Shewhart-type control charts with outlier-resistant alternatives — such as the median, MAD, or trimmed statistics — so that isolated contaminating observations or non-normal process distributions do not inflate control limits and mask genuine process shifts.