Linganisha mbinu

	Uchambuzi wa uwezo wa mchakato mseto	Uchambuzi Imara wa Uwezo wa Mchakato
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	1990s–2000s
Mwanzilishi	Various; systematised through extensions of Kane (1986) and Pearn, Kotz & Johnson (1992)	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)
Aina	Quantitative process quality assessment	Quantitative quality engineering method
Chanzo asilia	Pearn, W. L., Kotz, S., & Johnson, N. L. (1992). Distributional and inferential properties of process capability indices. Journal of Quality Technology, 24(4), 216–231.	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.
Majina mbadala	hybrid PCA, integrated process capability analysis, combined capability index analysis, multi-method process capability assessment	Robust PCA, Robust Capability Indices, Outlier-Resistant Capability Analysis, Robust Cpk Analysis
Zinazohusiana	6	6
Muhtasari	Hybrid process capability analysis combines two or more capability assessment techniques — for example, classical indices (Cp, Cpk) with fuzzy logic, bootstrap inference, or Bayesian estimation — to overcome the limitations of any single approach. By integrating complementary methods, it delivers more robust capability statements for non-normal, asymmetric, or short-run processes where standard indices alone would mislead quality decisions.	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.