Abstract
In many complex processes, such as semiconductor manufacturing or production of mass storage systems, a large number of variables are monitored simultaneously. These variables can typically be impacted by several points of the manufacturing process, necessitating efforts that include not only monitoring but also diagnostics involving establishing change-points, regimes and potential stages of influence. We discuss statistical methods used to handle such multi-stage data and give examples of applying these methods in large-scale monitoring systems.
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References
Bagshaw, M., & Johnson, R. A. (1975). The effect of serial correlation on the performance of CUSUM tests II. Technometrics, 17, 73–80.
Baseman, R. J., Hoffman, W. K., Ruegsegger, S., & Yashchin, E. (2010). System for monitoring multi-orderable measurement data. US Patent Publication 20100017009A1.
Capizzi, G. (2015). Recent advances in process monitoring: Nonparametric and variable-selection methods for phase I and phase II (with discussion). Quality Engineering, 27, 44–80.
Civil, A. D., Komatsu, J. G., Ng, A. S., Liang, Y., Wargo, J., Yashchin, E., et al. (2013). Hybrid analysis of emerging trends for process control. US Patent Publication 20130041626A1.
Cox, D. R., & Miller, H. D. (1977). The theory of stochastic processes. Boca Raton, FL: Chapman & Hall/CRC.
Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116–128.
Golosnoy, V., Ragulin, S., & Schmid, W. (2011). CUSUM control charts for monitoring optimal portfolio weights. Computational Statistics and Data Analysis, 55(11), 2991–3009.
Hawkins, D. M., & Olwell, D. H. (1998). Cumulative sum charts and charting for quality improvement. New York: Springer.
Hryniewicz, O., & Kaczmarek, K. (2016). Monitoring of short series of dependent observations using a control chart approach and data mining techniques. In S. Knoth (Ed.), Proceedings of the XII International Workshop on Intelligent Statistical Quality Control (pp. 143–161).
Moustakides, G. V. (1986). Optimal stopping times for detecting changes in distributions. Annals of Statistics, 14(4), 1379–1387.
Negandhi, V., Sreenivasan, L., Giffen, R., Sewak, M., & Rajasekharan, A. (2015). IBM predictive maintenance and quality 2.0 technical overview. Armonk, NY: IBM Redbooks.
Philips, T., Yashchin, E., & Stein, D. (2003). Using statistical process control to monitor active managers. Journal of Portfolio Management, 30(1), 86–94.
Shi, J., & Zhou, S. (2009). Quality control and improvement for multistage systems: A survey. IIE Transactions, 41(9), 744–753.
Shmueli, G., & Burkom, H. (2010). Statistical challenges facing early outbreak detection in biosurveillance. Technometrics, 52(1), 39–51.
Siegmund, D. (1985). Sequential analysis. New York: Springer.
Sparks, R. (2015). Social network monitoring: Aiming to identify periods of unusually increased communications between parties of interest. In S. Knoth & W. Schmid (Eds.), Frontiers in statistical quality control (Vol. 11, pp. 3–13). Berlin: Springer.
Woodall, W. H., & Montgomery, D. C. (2014). Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology, 46(1), 78–94.
Yashchin, E. (1985). On analysis and design of Cusum-Shewhart control schemes. IBM Journal of Research and Development, 29, 377–391.
Yashchin, E. (2012). Design and implementation of systems for monitoring lifetime data. In H. J. Lenz, W. Schmid & P. Wilrich (Eds.), Frontiers in statistical quality control (Vol. 10, pp. 171–195). Berlin: Springer.
Acknowledgements
I would like to thank Aaron Civil, Reynaldo Corral, Jeff Komatsu, Tony Spielberg, John Wargo and Paul Zulpa from the IBM Supply Chain organization for their kind help, feedback and effort in developing a solution based on this methodology. I am also thankful to David L. Jensen and Brian F. White (IBM Research) for help with software design and development and to Robert J. Baseman for his valuable advice and feedback. My deepest appreciation goes to Steven Ruegsegger and William K. Hoffman from the IBM Microelectronics organization for help in developing software and for integrating the methodology into the ecosystem of tools. I am most thankful to Ishan Sehgal and Jayashree Ravichandran (IBM Internet of Things) for their help and support in productizing this methodology. I am also very indebted to the Editor and the Referee for their insightful comments and suggestions.
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Yashchin, E. (2018). Statistical Monitoring of Multi-Stage Processes. In: Knoth, S., Schmid, W. (eds) Frontiers in Statistical Quality Control 12. Frontiers in Statistical Quality Control. Springer, Cham. https://doi.org/10.1007/978-3-319-75295-2_11
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DOI: https://doi.org/10.1007/978-3-319-75295-2_11
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