방법 비교

	강건 통계적 공정 관리	강건 공정능력 분석
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	1989–1990s (formalized in peer-reviewed literature)	1990s–2000s
창시자	Rocke, D. M.; Tatum, L. G. (key contributors)	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)
유형	Robust statistical monitoring framework	Quantitative quality engineering method
원전	Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141.	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.
별칭	Robust SPC, Resistant SPC, Outlier-robust process monitoring, Robust process surveillance	Robust PCA, Robust Capability Indices, Outlier-Resistant Capability Analysis, Robust Cpk Analysis
관련	5	6
요약	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.	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.