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Statistical control of multiple-stream processes: a literature review. (English) Zbl 1331.62496

Knoth, Sven (ed.) et al., Frontiers in statistical quality control 11. Selected papers based on the presentations at the XIth international workshop on intelligent statistical quality control, Sydney, Australia, August 20–23, 2013. Cham: Springer (ISBN 978-3-319-12354-7/hbk; 978-3-319-12355-4/ebook). 49-64 (2015).
Summary: This paper presents a survey of the research on techniques for the statistical control of industrial multiple-stream processes – processes in which the same type of item is manufactured in several streams of output in parallel, or still continuous processes in which several measures are taken at a cross section of the product. The literature on this topic is scarce, with few advances since 1950, and experiencing a resurgence from the mid-1990s. Essential differences in the underlying models of works before and after 1995 are stressed, and issues for further research are pointed out.
For the entire collection see [Zbl 1337.62009].

MSC:

62P30 Applications of statistics in engineering and industry; control charts
62-02 Research exposition (monographs, survey articles) pertaining to statistics
Full Text: DOI

References:

[1] Amin, R.; Wolff, H.; Besenfelder, W.; Baxley, R. Jr, EWMA control charts for the smallest and largest observations, Journal of Quality Technology, 31, 2, 189-206 (1999)
[2] Benjamini, Y.; Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society, Series B, 57, 289-300 (1995) · Zbl 0809.62014
[3] Bothe, D. R (2008). Process Capability Indices for Multiple Stream Processes. In Encyclopedia of statistics in quality and reliability. New York: Wiley
[4] Boyd, D. F., Applying the group chart for \(\bar{X}\) and R, Industrial Quality Control, 7, 22-25 (1950)
[5] Burkom, H. S., Murphy, S., Coberly, J., & Hurt-Mullen, K. (2005). Public health monitoring tools for multiple data streams. In Syndromic Surveillance: Reports from a National Conference, 2004 (54 (Suppl.), pp. 55-62). Morbidity and Mortality Weekly Report 2005.
[6] Burr, I. W., Statistical quality control methods (1976), New York: Marcel Dekker, New York · Zbl 0357.62086
[7] Colbeck, J. P. (1999). Some alternative methods for monitoring multiple-stream processes. (Electronic thesis and dissertations). Department of Statistics, University of Manitoba, Winnipeg. (Found in http://hdl.handle.net/1993/1941).
[8] Costa, A. F. B (1997). \( \bar{X}\) Charts with variable sample size and sampling intervals. Journal of Quality Technology, 29, 197-204.
[9] Dunn, O. J., Estimation of the means of dependent variables, Annals of Mathematical Statistics, 29, 2775-279 (1958) · Zbl 0092.36702 · doi:10.1214/aoms/1177706443
[10] Epprecht, E. K.; Barros, I. P., Monitoring a Multiple Stream Process with Varying Means. Technical Memorandum 01/2013 (2013), PUC-Rio, Rio de Janeiro: Department of Industrial Engineering, PUC-Rio, Rio de Janeiro
[11] Epprecht, E. K., Barbosa, L. F. M., & Simões, B. F. T. (2011). SPC of multiple stream processes—a chart for enhanced detection of shifts in one stream. Produção, 21(2), 242-253. doi:10.1590/S0103-65132011005000022.
[12] Grimshaw, S. D.; Bryce, G. R.; Meade, D. J., Control limits for group charts, Quality Engineering, 12, 2, 177-184 (1999) · doi:10.1080/08982119908962575
[13] Jensen, W. A.; Jones-Farmer, L. A.; Champ, C. W.; Woodall, W. H., Effects of parameter estimation on control chart properties: a literature review, Journal of Quality Technology, 38, 4, 349-364 (2006)
[14] Jirasettapong, P.; Rojanarowan, P., A guideline to select control charts for multiple stream processes control, Engineering Journal, 15, 3, 1-14 (2011) · doi:10.4186/ej.2011.15.3.1
[15] Johnson, R. A.; Wichern, D. W., Applied multivariate statistical analysis (2007), New York: Pearson Prentice Hall, New York · Zbl 1269.62044
[16] Lanning, J. W.; Montgomery, D. C.; Runger, G. C., Monitoring a multiple stream filling operation using fractional samples, Quality Engineering, 15, 2, 183-195 (2002) · doi:10.1081/QEN-120015851
[17] Lasi, G., Mongiello, C., & Scagliarini, M. (2004). Il controllo statistico per processi a flussi multipli: problemi e soluzioni di un caso aziendale. Statistica, anno LXIV(4), 707-719 (In Italian). · Zbl 1207.62233
[18] Liu, X.; MacKay, R. J.; Steiner, S. H., Monitoring Multiple Stream Processes, Quality Engineering, 20, 296-308 (2008) · doi:10.1080/08982110802035404
[19] Marshall, C.; Best, N.; Bottle, A.; Aylin, P., Statistical Issues in the Prospective Monitoring of Health Outcomes Across Multiple Units, Journal of the Royal Statistical Society, Series A, 167, 3, 541-559 (2004) · doi:10.1111/j.1467-985X.2004.apm10.x
[20] Mei, Y., Efficient scalable systems for monitoring a large number of data streams, Biometrika, 97, 2, 419-433 (2010) · Zbl 1406.62088 · doi:10.1093/biomet/asq010
[21] Meneces, N. S.; Olivera, S. A.; Saccone, C. D.; Tessore, J., Statistical Control of Multiple-Stream Processes: A Shewhart Control Chart for Each Stream, Quality Engineering, 20, 185-194 (2008) · doi:10.1080/08982110701241608
[22] Montgomery, D. C., Introduction to statistical quality control (2012), New york: Wiley, New york
[23] Mortell, R. R.; Runger, G. C., Statistical Process control for multiple stream processes, Journal of Quality Technology, 27, 1, 1-12 (1995)
[24] Nelson, L. S., Control Chart for multiple stream processes, Journal of Quality Technology, 18, 4, 225-226 (1986)
[25] Ott, E. R.; Snee, R. D., Identifying useful differences in a multiple-head machine, Journal of Quality Technology, 5, 2, 47-57 (1973)
[26] Prabhu, S. S.; Montgomery, D. C.; Runger, G. C., A combined adaptive sample size and sampling interval control scheme, Journal of Quality Technology, 26, 164-176 (1994)
[27] Pyzdek, T. (1992). Pyzdek’s Guide to SPC, Vol. Two: Applications and Special Topics. ASQ-Quality Press: Milwaukee, WI, and Quality Publishing, Inc.: Tucson, AZ.
[28] Runger, G. C.; Alt, F. B.; Montgomery, D. C., Controlling multiple stream processes with principal components, International Journal of Production Research, 34, 11, 2991-2999 (1996) · Zbl 0927.90040 · doi:10.1080/00207549608905074
[29] Sidak, Z., Rectangular Confidence Regions for the Means of Multivariate Normal Distribution, Journal of the American Statistical Association, 62, 626-633 (1967) · Zbl 0158.17705
[30] Simões, B. F. T. (2010). Controle Estatístico de Processos Multicanal. (Doctoral thesis). Departamento de Engenharia Industrial, PUC-Rio, Rio de Janeiro (In Portuguese).
[31] Tartakovsky, A. G.; Rozovskha, B. L.; Blazeka, R. B.; Kim, H., Detection of intrusions in information systems by sequential change-point methods (with Discussion), Statistical Methodology, 3, 252-340 (2006) · Zbl 1248.94032 · doi:10.1016/j.stamet.2005.05.003
[32] Wise, S. A.; Fair, D. C., Innovative control charting-practical spc solutions for today’s manufacturing environment (1998), Milwaukee, WI: ASQ-Quality Press, Milwaukee, WI
[33] Wludyka, P. S. (2002). Controlling Non-Homogeneous Multistream Binomial Processes with a Chi-Squared Control Chart. In Proceedings of the ASA Meeting 2002, Quality and Productivity Research Conference, Tempe, Arizona [CD-ROM].
[34] Wludyka, P. S.; Jacobs, S. L., Runs rules and p-charts for binomial multistream processes, Communications in Statistics: Simulation and Computation, 31, 1, 97-142 (2002) · Zbl 1081.62578 · doi:10.1081/SAC-120002719
[35] Wludyka, P. S., Jacobs, S. L. (2002b). Controlling homogeneous multistream binomial processes with a chi-squared control chart. In Proceedings of the 33rd Annual Meeting of the Decision Sciences Institute (pp.2254-2263). San Diego, California, [CD-ROM].
[36] Woodall, W. H.; Ncube, M. M., Multivariate CUSUM Quality Control Procedures, Tecnometrics, 27, 3, 285-292 (1985) · Zbl 0595.62106 · doi:10.1080/00401706.1985.10488053
[37] Woodall, W. H.; Grigg, O. A.; Burkom, H. S.; Lenz, H.-J.; Wilrich, P.-T.; Schmid, W., Research issues and ideas on health-related surveillance, Frontiers in Statistical Quality Control 9, 145-155 (2010), Heidelberg: Physica-Verlag, Heidelberg · doi:10.1007/978-3-7908-2380-6_10
[38] Xiang, L.; Tsung, F., Statistical monitoring of multi-stage processes based on engineering models, IIE Transactions, 40, 10, 957-970 (2008) · doi:10.1080/07408170701880845
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