Monitoring univariate processes using control charts: Some practical issues and advice

References

• Alla, S., and S. K. Adari. 2021. What is mlops?. In Beginning MLOps with MLFlow, 79–124. Berkeley, CA: Apress.
   Google Scholar
• Alt, F. B., J. J. Goode, and H. M. Wadsworth. 1976. Small sample probability limits for the mean of a multivariate normal process. In ASQC Technical Conference Transactions, 170–76. Toronto: American Society for Quality Control.
   Google Scholar
• Amiri, A., and S. Allahyari. 2012. Change point estimation methods for control chart postsignal diagnostics: A literature review. Quality and Reliability Engineering International 28 (7):673–85. doi:10.1002/qre.1266.
   Web of Science ®Google Scholar
• Antzoulakos, D. L., and A. C. Rakitzis. 2007. The revised m-of-k runs rules. Quality Engineering 20 (1):75–81. doi:10.1080/08982110701636401.
   Google Scholar
• Apley, D. W. 2012. Posterior distribution charts: A Bayesian approach for graphically exploring a process mean. Technometrics 54 (3):279–93. doi:10.1080/00401706.2012.694722.
   Web of Science ®Google Scholar
• ASTM E2587-16. 2021. Standard Practice for Use of Control Charts in Statistical Process Control.
   Google Scholar
• Automotive Industry Action Group (AIAG). 2010. Measurement System Analysis.
   Google Scholar
• Burdick, R. K., C. M. Borror, and D. C. Montgomery. 2003. A review of methods for measurement systems capability analysis. Journal of Quality Technology 35 (4):342–54. doi:10.1080/00224065.2003.11980232.
   Web of Science ®Google Scholar
• Capizzi, G., and G. Masarotto. 2013. Phase I distribution-free analysis of univariate data. Journal of Quality Technology 45 (3):273–84. doi:10.1080/00224065.2013.11917938.
   Web of Science ®Google Scholar
• Capizzi, G., and G. Masarotto. 2020. Guaranteed in-control control chart performance with cautious parameter learning. Journal of Quality Technology 52 (4):385–403. doi:10.1080/00224065.2019.1640096.
   Web of Science ®Google Scholar
• Chakraborti, S., S. W. Human, and M. A. Graham. 2008. Phase I Statistical Process Control Charts: An overview and some results. Quality Engineering 21 (1):52–62. doi:10.1080/08982110802445561.
   Web of Science ®Google Scholar
• Cokelaer, T. 2022. fitter: v1.5.1. https://zenodo.org/badge/latestdoi/23078551
   Google Scholar
• Colosimo, B. M., and Del Castillo, E. (Eds.). 2006. Bayesian Process Monitoring, Control and Optimization. Boca Raton, FL: CRC Press.
   Google Scholar
• Crowder, S. V., D. M. Hawkins, M. R. Reynolds, Jr, and E. Yashchin. 1997. Process control and statistical inference. Journal of Quality Technology 29 (2):134–9. doi:10.1080/00224065.1997.11979742.
   Web of Science ®Google Scholar
• Delignette-Muller, M. L., and C. Dutang. 2015. fitdistrplus: An R package for fitting distributions. Journal of Statistical Software 64 (4):1–34. doi:10.18637/jss.v064.i04.
   Web of Science ®Google Scholar
• Does, R. J. M. M., R. Goedhart, and W. H. Woodall. 2020. On the design of control charts with guaranteed conditional performance under estimated parameters. Quality and Reliability Engineering International 36 (8):2610–20. doi:10.1002/qre.2658.
   Google Scholar
• Fraker, S. E., W. H. Woodall, and S. Mousavi. 2008. Performance Metrics for Surveillance Schemes. Quality Engineering 20 (4):451–64. doi:10.1080/08982110701810444.
   Google Scholar
• Grant, E. L., and R. S. Leavenworth. 1980. Statistical Quality Control, 5th ed. New York: McGraw-Hill Book Company.
   Google Scholar
• Hawkins, D. M. 1987. Self-starting CUSUM charts for location and scale. Journal of the Royal Statistical Society: Series D (the Statistician) 36 (4):299–316.
   Google Scholar
• Hawkins, D. M., and D. H. Olwell. 1998. Cumulative sum charts and charting for quality improvement. New York, NY: Springer.
   Google Scholar
• Hotelling, H. 1947. Multivariate quality control-Illustrated by the air testing of sample bombsights. In Techniques of Statistical Analysis. eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, 111–84. New York: McGraw-Hill.
   Google Scholar
• Hou, Y., B. He, X. Zhang, Y. Chen, and Q. Yang. 2020. A new Bayesian scheme for self-starting process mean monitoring. Quality Technology & Quantitative Management 17 (6):661–84. doi:10.1080/16843703.2020.1726052.
   Web of Science ®Google Scholar
• Huberts, L. C., R. Goedhart, and R. J. M. M. Does. 2022. Improved control chart performance using cautious parameter learning. Computers & Industrial Engineering 169:108185. doi:10.1016/j.cie.2022.108185.
   Web of Science ®Google Scholar
• ISO 7870-1:2019. 2019. Control charts—Part 1: General guidelines.
   Google Scholar
• Jalilibal, Z., A. Amiri, P. Castagliola, and M. B. Khoo. 2021. Monitoring the coefficient of variation: A literature review. Computers & Industrial Engineering 161:107600. doi:10.1016/j.cie.2021.107600.
   Web of Science ®Google Scholar
• Jensen, W. A., L. A. Jones-Farmer, C. W. Champ, and W. H. Woodall. 2006. Effects of parameter estimation on control chart properties: A literature review. Journal of Quality Technology 38 (4):349–64. doi:10.1080/00224065.2006.11918623.
   Web of Science ®Google Scholar
• Jones-Farmer, L. A., J. D. Ezell, and B. T. Hazen. 2014. Applying control chart methods to enhance data quality. Technometrics 56 (1):29–41. doi:10.1080/00401706.2013.804437.
   Web of Science ®Google Scholar
• Jones-Farmer, L. A., W. H. Woodall, S. H. Steiner, and C. W. Champ. 2014. An overview of phase I analysis for process improvement and monitoring. Journal of Quality Technology 46 (3):265–80. doi:10.1080/00224065.2014.11917969.
   Web of Science ®Google Scholar
• Keefe, M. J., W. H. Woodall, and L. A. Jones-Farmer. 2015. The conditional in-control performance of self-starting control charts. Quality Engineering 27 (4):488–99. doi:10.1080/08982112.2015.1065323.
   Web of Science ®Google Scholar
• Khakifirooz, M., V. G. Tercero-Gómez, and W. H. Woodall. 2021. The role of the normal distribution in statistical process monitoring. Quality Engineering 33 (3):497–510. doi:10.1080/08982112.2021.1909731.
   Web of Science ®Google Scholar
• Knoth, S. 2016. The case against the use of synthetic control charts. Journal of Quality Technology 48 (2):178–95. doi:10.1080/00224065.2016.11918158.
   Web of Science ®Google Scholar
• Knoth, S. 2022. An expanded case against synthetic‐type control charts. Quality and Reliability Engineering International 38 (6):3197–215. doi:10.1002/qre.3128.
   Web of Science ®Google Scholar
• Knoth, S., M. A. Mahmoud, N. A. Saleh, V. G. Tercero-Gómez, and W. H. Woodall. 2022. Letter on statistical process monitoring research: Misdirections and recommendations. Quality and Reliability Engineering International 38 (4):2198–9. doi:10.1002/qre.3064.
   Web of Science ®Google Scholar
• Knoth, S., N. A. Saleh, M. A. Mahmoud, W. H. Woodall, and V. G. Tercero-Gómez. 2023. A critique of a variety of “memory-based” process monitoring methods. Journal of Quality Technology 55 (1):18–42. doi:10.1080/00224065.2022.2034487.
   Web of Science ®Google Scholar
• Lamooki, S. R., J. Kang, L. A. Cavuoto, F. M. Megahed, and L. A. Jones-Farmer. 2021. Personalized and nonparametric framework for detecting changes in gait cycles. IEEE Sensors Journal 21 (17):19236–46. doi:10.1109/JSEN.2021.3090985.
   Web of Science ®Google Scholar
• Li, J. 2022. Adaptive CUSUM chart with cautious parameter learning. Quality and Reliability Engineering International 38 (6):3135–56. doi:10.1002/qre.3116.
   Web of Science ®Google Scholar
• Lucas, J. M. 1982. Combined Shewhart-CUSUM quality control schemes. Journal of Quality Technology 14 (2):51–9. doi:10.1080/00224065.1982.11978790.
   Web of Science ®Google Scholar
• Lucas, J. M., and M. S. Saccucci. 1990. Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics 32 (1):1–12. doi:10.1080/00401706.1990.10484583.
   Web of Science ®Google Scholar
• Mahmoud, M. A., G. R. Henderson, E. K. Epprecht, and W. H. Woodall. 2010. Estimating the standard deviation in quality control applications. Journal of Quality Technology 42 (4):348–57. doi:10.1080/00224065.2010.11917832.
   Web of Science ®Google Scholar
• Megahed, F., L. Wells, and J. Camelio. 2010. The use of 3D laser scanners in statistical process control (No. 2010-01-1864). SAE Technical Paper.
   Google Scholar
• Megahed, F. M., and L. A. Jones-Farmer. 2015. Statistical perspectives on “big data. In Frontiers in statistical quality control 11, 29–47. Cham: Springer.
   Google Scholar
• Megahed, F. M. 2019. Discussion of “Real-time monitoring of events applied to syndromic surveillance. Quality Engineering 31 (1):97–104. doi:10.1080/08982112.2018.1530358.
   Web of Science ®Google Scholar
• Mohammed, M. A., P. Worthington, and W. H. Woodall. 2008. Plotting basic control charts: Tutorial notes for healthcare practitioners. Quality & Safety in Health Care 17 (2):137–45. doi:10.1136/qshc.2004.012047.
   PubMedGoogle Scholar
• Montgomery, D. C. 2019. Introduction to statistical quality control, 8th ed. Hoboken, NJ: John Wiley & Sons, Inc.
   Google Scholar
• Noorossana, R., Saghaei, A., and Amiri, A., (Eds.). 2011. Statistical analysis of profile monitoring. Hoboken, NJ: John Wiley & Sons, Inc.
   Google Scholar
• Page, E. S. 1954. Continuous inspection schemes. Biometrika 41 (1-2):100–15. doi:10.1093/biomet/41.1-2.100.
   Web of Science ®Google Scholar
• Psarakis, S., A. K. Vyniou, and P. Castagliola. 2014. Some recent developments on the effects of parameter estimation on control charts. Quality and Reliability Engineering International 30 (8):1113–29. doi:10.1002/qre.1556.
   Web of Science ®Google Scholar
• Psarakis, S., and G. E. A. Papaleonida. 2007. SPC procedures for monitoring autocorrelated processes. Quality Technology & Quantitative Management 4 (4):501–40. doi:10.1080/16843703.2007.11673168.
   Google Scholar
• Qiu, J., Y. Lin, and I. M. Zwetsloot. 2023. Sensor Data Monitoring Using LSTM-Based Shewhart-Type Control Charts. Working Paper.
   Google Scholar
• Quesenberry, C. P. 1991. SPC Q charts for start-up processes and short or long runs. Journal of Quality Technology 23 (3):213–24. doi:10.1080/00224065.1991.11979327.
   Web of Science ®Google Scholar
• Quesenberry, C. P. 1995. On properties of Q charts for variables. Journal of Quality Technology 27 (3):184–203. doi:10.1080/00224065.1995.11979592.
   Web of Science ®Google Scholar
• Quesenberry, C. P. 1997. SPC Methods for Quality Improvement. Hoboken, NJ: John Wiley & Sons Incorporated.
   Google Scholar
• Rigdon, S. E., E. N. Cruthis, and C. W. Champ. 1994. Design strategies for individuals and moving range control charts. Journal of Quality Technology 26 (4):274–87. doi:10.1080/00224065.1994.11979539.
   Web of Science ®Google Scholar
• Roberts, S. W. 1959. Control chart tests based on geometric moving averages. Technometrics 1 (3):239–50. doi:10.1080/00401706.1959.10489860.
   Google Scholar
• Sall, J. 2018. Scaling-up process characterization. Quality Engineering 30 (1):62–78. doi:10.1080/08982112.2017.1361539.
   Web of Science ®Google Scholar
• Santiago, E., and J. Smith. 2013. Control charts based on the exponential distribution: Adapting runs rules for the t chart. Quality Engineering 25 (2):85–96. doi:10.1080/08982112.2012.740646.
   Web of Science ®Google Scholar
• Shewhart, W. A. 1931. Economic control of quality of manufactured product. London: Macmillan and Co. Ltd.
   Google Scholar
• Shewhart, W. A. 1939. Statistical method from the viewpoint of quality control. Washington, DC: Graduate School of the Department of Agriculture (Republished in 1986 by Dover Publications, Inc., Mineola, NY.).
   Google Scholar
• Sullivan, J. H., and L. A. Jones. 2002. A self-starting control chart for multivariate individual observations. Technometrics 44 (1):24–33. doi:10.1198/004017002753398290.
   Web of Science ®Google Scholar
• Wang, Z., and I. M. Zwetsloot. 2021. Exploring the usefulness of functional data analysis for health surveillance. In Frontiers in Statistical Quality Control 13, 247–64. New York: Springer International Publishing.
   Google Scholar
• Wasserman, G. S. 1995. An Adaptation of the EWMA Chart for Short Run SPC. International Journal of Production Research 33:2821–33.
   Web of Science ®Google Scholar
• Western Electrical Company 1956. Statistical Quality Control Handbook. Indianapolis, IN: Western Electric Corporation.
   Google Scholar
• Wheeler, D. J. 2006. EMP III (Evaluating the Measurement Process): Using Imperfect Data, Knoxville, TN: SPC Press.
   Google Scholar
• Wheeler, D. J. 2010. Are you sure we don’t need normally distributed data? Quality Digest: www.spcpress.com/pdf/DJW220.pdf.
   Google Scholar
• Wheeler, D. J. 2011. What about p-Charts? Quality Digest. https://www.qualitydigest.com/author_content/12852.
   Google Scholar
• Woodall, W. H. 2017. Bridging the gap between theory and practice in basic statistical process monitoring. Quality Engineering 29 (1):2–15.
   Web of Science ®Google Scholar
• Woodall, W. H., and D. C. Montgomery. 2014. Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology 46 (1):78–94. doi:10.1080/00224065.2014.11917955.
   Web of Science ®Google Scholar
• Woodall, W. H., and F. W. Faltin. 2019. Rethinking control chart design and evaluation. Quality Engineering 31 (4):596–605. doi:10.1080/08982112.2019.1582779.
   Web of Science ®Google Scholar
• Woodall, W. H., F. W. Faltin, and E. Yashchin. 2022. The Importance of Tuning Your Control Charts. Quality Progress 55 (9):8–35.
   Google Scholar
• Woodall, W. H., L. Kodali, and E. Yashchin. 2018. Discussion of “Scaling up process characterization” by John Sall. Quality Engineering 30 (1):88–92. doi:10.1080/08982112.2017.1381487.
   Web of Science ®Google Scholar
• Zwetsloot, I., and W. H. Woodall. 2021. A review of some sampling and aggregation strategies for basic statistical process monitoring (with discussion). Journal of Quality Technology 53 (1):1–16. doi:10.1080/00224065.2019.1611354.
   Web of Science ®Google Scholar
• Zwetsloot, I. M., Y. Lin, J. Qiu, L. Li, W. K. F. Lee, E. Y. S. Yeung, C. Y. W. Yeung, and C. C. L. Wong. 2023. Remaining useful life modeling with an escalator health condition analytic system. Submitted, arxiv.org/abs/2306.05436.
   Google Scholar