Сравнение на методи

	Анализ на възможностите на процеса с робастни методи	Устойчив статистически контрол на процесите
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1990s–2000s	1989–1990s (formalized in peer-reviewed literature)
Създател	Extended from classical PCA (Kane, 1986; Juran, 1974) via robust statistics (Huber, 1981); formalized for capability indices by Tong & Chen (1998) and Pearn & Kotz (1994)	Rocke, D. M.; Tatum, L. G. (key contributors)
Тип	Quantitative quality engineering method	Robust statistical monitoring framework
Основополагащ източник	Maravelakis, P. E., Bersimis, S., Panaretos, J., & Psarakis, S. (2004). Identifying the out of control variable in a multivariate control chart. Communications in Statistics - Theory and Methods, 33(10), 2499–2510.	Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141.
Други названия	Robust PCA, Robust Capability Indices, Outlier-Resistant Capability Analysis, Robust Cpk Analysis	Robust SPC, Resistant SPC, Outlier-robust process monitoring, Robust process surveillance
Свързани	6	5
Резюме	Robust process capability analysis extends classical capability indices (Cp, Cpk, Ppk) by replacing the sample mean and standard deviation with robust location and scale estimators — such as the median, trimmed mean, MAD, or IQR-based spread — so that outliers and non-normal process distributions do not inflate or distort the capability estimate. The result is a more reliable assessment of whether a manufacturing or service process can consistently meet specification limits.	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.