As is well known, data obtained from real-world processes are typically autocorrelated, and monitoring such processes requires accounting for this autocorrelation. The control charts developed so far for monitoring the defective proportion (p) are generally based on the assumption that binary observations are independent, which ignores the inherent correlation in the data. In this paper, a cumulative sum (CUSUM) control chart is proposed that incorporates the autocorrelation between binary observations using a first-order two-state Markov chain model. Furthermore, using the Average Number of Observations to Signal (ANOS) index, it is shown that under 100% inspection conditions, the proposed chart performs better than the Bernoulli CUSUM chart—which assumes independent observations—and, in other words, detects increases in p more quickly.
Doukohaki,P. and Noorelsana,R. (2012). Monitoring Defective Rate of Autocorrelated Binary Data Under 100% Inspection Conditions. Journal of Quality Engineering and Management, 2(1), 56-63.
MLA
Doukohaki,P. , and Noorelsana,R. . "Monitoring Defective Rate of Autocorrelated Binary Data Under 100% Inspection Conditions", Journal of Quality Engineering and Management, 2, 1, 2012, 56-63.
HARVARD
Doukohaki P., Noorelsana R. (2012). 'Monitoring Defective Rate of Autocorrelated Binary Data Under 100% Inspection Conditions', Journal of Quality Engineering and Management, 2(1), pp. 56-63.
CHICAGO
P. Doukohaki and R. Noorelsana, "Monitoring Defective Rate of Autocorrelated Binary Data Under 100% Inspection Conditions," Journal of Quality Engineering and Management, 2 1 (2012): 56-63,
VANCOUVER
Doukohaki P., Noorelsana R. Monitoring Defective Rate of Autocorrelated Binary Data Under 100% Inspection Conditions. J. Qual. Eng. Manag., 2012; 2(1): 56-63.
