Statistical quality control aims to achieve the product or process quality by utilizing statistical techniques, in which statistical process control (SPC) has been demonstrated to be one primary tool for monitoring the process or product quality. Since 1920s, the control chart, as one of the most important SPC techniques, has been widely studied.
Univariate Control Charts Versus Multivariate Control Charts
In terms of the number of variables, control charts can be classified into two types, that is, univariate control charts and multivariate control charts.
The performance of the conventional univariate control charts, including Shewhart control charts, cumulative sum (CUSUM) control charts and exponentially weighted moving average (EWMA) control charts have been extensively reviewed. The research demonstrates that the Shewhart chart is more sensitive to large shifts than the EWMA and CUSUM chart and vice versa. These traditional control charts usually assume that the observations are...
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References and Further Reading
Apte UM, Reynolds CC (1995) Quality management at Kentucky fried chicken. Interfaces 25:6–21
Bersimis S, Psarakis S, Panaretos J (2007) Multivariate statistical process control charts: an overview. Quality Reliab Eng Int 23(5):517–543
Cartwright G, Hogg B (1996) Measuring processes for profit. The TQM Magazine 8(1):26–30
Chakraborti S, Van der Laan P, Bakir ST (2001) Nonparametric control charts: an overview and some results. J Qual Technol 33:304–315
Crosier RB (1988) Multivariate generalizations of cumulative sum quality-control schemes. Technometrics 30(3):291–303
Green RS (1999) The application of statistical process control to manage global client outcomes in behavioral healthcare. Eval Program Plann 22:199–210
Han D, Tsung F (2005) Comparison of the Cuscore. GLRT and CUSUM control charts for detecting a dynamic mean change. Ann Inst Stat Math 57:531–552
Han D, Tsung F (2006) A reference-free cuscore chart for dynamic mean change detection and a unified framework for charting performance comparison. J Am Stat Assoc 101:368–386
Han D, Tsung F (2007) Detection and diagnosis of unknown abrupt changes using CUSUM multi-chart schemes. Sequential Anal 26:225–249
Han D, Tsung F (2009) Run length properties of the CUSUM and EWMA control schemes for stationary autocorrelated processes. Statistica Sinica 19:473–490
Han D, Tsung F, Li Y (2007a) A CUSUM chart with local signal amplification for detecting a range of unknown shifts. Int J Reliab Qual Saf Eng 14:81–97
Han D, Tsung F, Hu X, Wang K (2007b) CUSUM and EWMA multi-charts for detecting a range of mean shifts. Statistica Sinica 17:1139–1164
Hawkins DM (1991) Multivariate quality control based on regression-adjusted variables. Technometrics 33:61–75
Hawkins DM (1993) Regression adjustment for variables in multivariate quality control. J Qual Technol 25:170–182
Hotelling H (1947) Multivariate quality control. In: Eisenhart C, Hastay M, Wallis WA (eds) Techniques of statistical analysis. McGraw-Hill, New York
Huwang L, Yeh AB, Wu C (2007) Monitoring multivariate process variability for individual observations. J Qual Technol 39(3):258–278
Jensen WA, Birch JB, Woodall WH (2009) Profile monitoring via nonlinear mixed models. J Qual Technol 41:18–34
Jin M, Tsung F (2009) A chart allocation strategy for multistage processes. IIE Trans 41(9):790–803
Kang L, Albin SL (2000) On-line monitoring when the process yields a linear profile. J Qual Technol 32:418–426
Kourti T, MacGregor JF (1996) Multivariate SPC methods for process and product monitoring. J Qual Technol 28(4):409–428
Li Y, Tsung F (2009) False discovery rate-adjusted charting schemes for multistage process monitoring and fault identification. Technometrics 51:186–205
Lowry A, Woodall WH, Champ CW, Rigdon SE (1992) A multivariate exponentially weighted moving average control chart. Technometrics 34(1):46–53
Maccarthy BL, Wasusri T (2002) A review of non-standard applications of statistical process control (SPC) charts. Int J Qual Reliab Manage 19(3):295–320
Mason RL, Tracy ND, Young JC (1995) Decomposition of T2 for multivariate control chart interpretation. J Qual Technol 27(2): 99–108
Mehring JS (1995) Achieving multiple timeliness goals for auto loans: a case for process control. Interfaces 25:81–91
Palm AC, Rodriguez RN, Spiring FA, Wheeler DJ (1997) Some perspectives and challenges for control chart methods. J Qual Technol 29:122–127
Pignatiello J, Runger GC (1990) Comparison of multivariate CUSUM charts. J Qual Technol 22:173–186
Shi J, Zhou S (2009) Quality control and improvement for multistage systems: a survey. IIE Trans 41:744–753
Shu LJ, Tsung F (2003) On multistage statistical process control. J Chin Inst Ind Eng 20:1–8
Shu LJ, Apley DW, Tsung F (2003) Autocorrelated process monitoring using triggered CUSCORE charts. Qual Reliab Eng Int 18:411–421
Shu LJ, Tsung F, Kapur KC (2004) Design of multiple cause-selecting charts for multistage processes with model uncertainty. Qual Eng 16:437–450
Shu LJ, Tsung F, Tsui KL (2005) Effects of estimation errors on cause-selecting charts. IIE Trans 37(6):559–567
Skinner KR, Montgomery DC, Runger GC (2003) Process monitoring for multiple count data using generalized linear model-based control charts. Int J Prod Res 41(6):1167–1180
Skinner KR, Montgomery DC, Runger GC (2004) Generalized linear model-based control charts for discrete semiconductor process data. Qual Reliab Eng Int 20:777–786
Sulek J (2004) Statistical quality control in services. Int J Serv Tech Manag 5:522–531
Sulek JM, Marucheck A, Lind MR (2005) Measuring performance in multi-stage service operations: an application of cause selecting control charts. J Oper Manag 24:711–727
Sullivan JH, Woodall WH (1996) A comparison of multivariate control charts for individual observations. J Qual Technol 28(4):398–408
Sun R, Tsung F (2003) A kernel-distance-based multivariate control chart using support vector methods. Int J Prod Res 41:2975–2989
Tracy ND, Young JC, Mason RL (1992) Multivariate control Charts for Individual Observations. J Qual Technol 24(2):88–95
Tsung F, Li Y, Jin M (2008) Statistical process control for multistage manufacturing and service operations: a review and some extensions. Int J Serv Oper Inform 3:191–204
Wade MR, Woodall WH (1993) A review and analysis of cause-selecting control charts. J Qual Technol 25(3):161–169
Wang K, Tsung F (2005) Using profile monitoring techniques for a data-rich environment with huge sample size. Qual Reliab Eng Int 21(7):677–688
Wang K, Tsung F (2007) Monitoring feedback-controlled processes using adaptive T2 schemes. Int J Prod Res 45:5601–5619
Wang K, Tsung F (2008) An adaptive T2 chart for monitoring dynamic systems. J Qual Technol 40:109–123
Wardell DG, Candia MR (1999) Statistical process monitoring of customer satisfactior survey data. Qual Manag J 3(4):36–50
Woodall WH (2006) The use of control charts in health-care and public-health surveillance. J Qual Technol 38(2):89–104
Woodall WH, Spitzner DJ, Montgomery DC, Gupta S (2004) Using control charts to monitor process and product quality profiles. J Qual Technol 36:309–320
Wyckoff DD (1984) New tools for achieving service quality. Cornell Hotel Rest Admin Quartr 25:78–91
Xiang L, Tsung F (2008) Statistical monitoring of multistage processes based on engineering models. IIE Trans 40(10):957–970
Yeh AB, Huwang L, Li YM (2009) Profile monitoring for binary response. IIE Trans 41(11):931–941
Zhang GX (1984) A new type of control charts and a theory of diagnosis with control charts. World Qual Congr Trans: 175–185
Zhang GX (1985) Cause-selecting control charts - a new type of quality control charts. The QR Journal 12:221–225
Zhang GX (1989) A new diagnosis theory with two kinds of quality. world quality congress transactions. Am Soc Qual Control 00:594–599
Zhang GX (1992) Cause-selecting control chart and diagnosis. Theory and practice. Aarhus School of Business. Department of Total Quality Management. Aarhus, Denmark
Zhao Y, Tsung F, Wang Z (2005) Dual CUSUM control schemes for detecting a range of mean shifts. IIE Trans 37:1047–1057
Zou C, Tsung F, Wang Z (2007a) Monitoring general linear profiles using multivariate EWMA schemes. Technometrics 49: 395–408
Zou C, Zhou C, Wang Z, Tsung F (2007b) A self-starting control chart for linear profiles. J Qual Technol 39:364–375
Zou C, Tsung F, Liu Y (2008a) A change point approach for phase I analysis in multistage processes. Technometrics 50(3): 344–356
Zou C, Tsung F, Wang Z (2008b) Monitoring profiles based on nonparametric regression methods. Technometrics 50: 512–526
Zou C, Wang Z, Tsung F (2008c) Monitoring an autocorrelated processes using variable sampling schemes at fixed-times. Qual Reliab Eng Int 24:55–69
Zuo Y, Serfling R (2000) General notions of statistical depth function. Annal Stat 28(2):461–482
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Tsung, F., Shang, Y., Ning, X. (2011). Statistical Quality Control: Recent Advances. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_83
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