Skip to main content
Log in

Effect of sample size on the performance of Shewhart control charts

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The control chart is one of the most powerful techniques in statistical process control (SPC) to monitor processes and ensure quality. The sample size n plays a critical role in the overall performance of any control chart. This article studies the effect of n on the performance of Shewhart control charts, which have traditionally been used for monitoring both the mean and variance of a variable (e.g., the diameter of a shaft and the temperature of a surface). The study is conducted under different combinations of false alarm rate and process shift. The detection speed of the Shewhart charts is evaluated in terms of average extra quadratic loss (AEQL) which is a measure of the overall performance. It is found that n = 2 is the best sample size of the Shewhart \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{R} \) and \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{S} \) charts. The comparative study reveals that the \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{R} \) and \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{S} \) charts with n = 2 outperform the \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{R} \) and \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{S} \) charts with n ≥ 4 by at least 9 and 7 %, respectively, in terms of AEQL. These results contradict the common knowledge in SPC niche that n between 4 and 6 is usually recommended for the \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{R} \) and \( \overset{\_}{\boldsymbol{X}}\&\boldsymbol{S} \) charts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Montgomery DC (2013) Introduction to statistical quality control. John Wiley & Sons, New York

    MATH  Google Scholar 

  2. Haridy S, Gouda SA, Wu Z (2011) An integrated framework of statistical process control and design of experiments for optimizing wire electrochemical turning process. Int J Adv Manuf Technol 53(1–4):191–207

    Article  Google Scholar 

  3. Castagliola P, Celano G, Fichera S, Nenes G (2013) The variable sample size t control chart for monitoring short production runs. Int J Adv Manuf Technol 66(9–12):1353–1366

    Google Scholar 

  4. Azam M, Aslam M, Jun C-H (2015) Designing of a hybrid exponentially weighted moving average control chart using repetitive sampling. Int J Adv Manuf Technol 77(9–12):1927–1933

    Article  Google Scholar 

  5. Costa AF, De Magalhaes MS (2007) An adaptive chart for monitoring the process mean and variance. Qual Reliab Eng Int 23(7):821–831

    Article  Google Scholar 

  6. Reynolds MR, Stoumbos ZG (1998) The SPRT chart for monitoring a proportion. IIE Trans 30(6):545–561

    Google Scholar 

  7. Domangue R, Patch SC (1991) Some omnibus exponentially weighted moving average statistical process monitoring schemes. Technometrics 33(3):299–313

    Article  MATH  Google Scholar 

  8. Wu Z, Tian Y (2005) Weighted-loss-function CUSUM chart for monitoring mean and variance of a production process. Int J Prod Res 43(14):3027–3044

    Article  MATH  Google Scholar 

  9. Wu Z, Yang M, Khoo MB, Yu F-J (2010) Optimization designs and performance comparison of two CUSUM schemes for monitoring process shifts in mean and variance. Eur J Oper Res 205(1):136–150

    Article  MathSciNet  MATH  Google Scholar 

  10. Costa AF, de Magalhães MS, Epprecht EK (2009) Monitoring the process mean and variance using a synthetic control chart with two-stage testing. Int J Prod Res 47(18):5067–5086

    Article  MATH  Google Scholar 

  11. Arnold JC, Reynolds M (2001) CUSUM control charts with variable sample sizes and sampling intervals. J Qual Technol 33(1):66–81

    Google Scholar 

  12. Luo H, Wu Z (2002) Optimal np control charts with variable sample sizes or variable sampling intervals. Economic Quality Control 17(1):39–61

    Article  MathSciNet  MATH  Google Scholar 

  13. Celano G, Costa A, Fichera S (2006) Statistical design of variable sample size and sampling interval\bar X control charts with run rulescontrol charts with run rules. Int J Adv Manuf Technol 28(9–10):966–977

    Article  Google Scholar 

  14. Kaya I (2009) A genetic algorithm approach to determine the sample size for attribute control charts. Inf Sci 179(10):1552–1566

    Article  Google Scholar 

  15. Wu Z, Yang M, Khoo MB, Castagliola P (2011) What are the best sample sizes for the Xbar and CUSUM charts? Int J Prod Econ 131(2):650–662

    Article  Google Scholar 

  16. Haridy S, Wu Z, Lee KM, Bhuiyan N (2013) Optimal average sample number of the SPRT chart for monitoring fraction nonconforming. Eur J Oper Res 229(2):411–421

    Article  Google Scholar 

  17. Reynolds MR, Arnold JC (2001) EWMA control charts with variable sample sizes and variable sampling intervals. IIE Trans 33(6):511–530

    Google Scholar 

  18. Lee P-H, Chang Y-C, Torng C-C (2012) A design of S control charts with a combined double sampling and variable sampling interval scheme. Communications in Statistics-Theory and Methods 41(1):153–165

    Article  MATH  Google Scholar 

  19. Reynolds MR Jr, Stoumbos ZG (2004) Control charts and the efficient allocation of sampling resources. Technometrics 46(2):200–214

    Article  MathSciNet  Google Scholar 

  20. Wu Z, Xie M, Tian Y (2002) Optimization design of the X & S charts for monitoring process capability. J Manuf Syst 21(2):83–92

    Article  Google Scholar 

  21. Montgomery DC, Woodall W (1999) Research issues and ideas in statistical process control. J Qual Technol 31(4):376–387

    Google Scholar 

  22. Reynolds MR, Amin RW, Arnold JC (1990) CUSUM charts with variable sampling intervals. Technometrics 32(4):371–384

    Article  MathSciNet  MATH  Google Scholar 

  23. Khoo MB, Tan EK, Chong ZL, Haridy S (2015) Side-sensitive group runs double sampling (SSGRDS) chart for detecting mean shifts. International Journal of Production Research (ahead-of-print):1–19

  24. Sparks RS (2000) CUSUM charts for signalling varying location shifts. J Qual Technol 32(2):157

    Google Scholar 

  25. Ou Y, Wu Z, Tsung F (2012) A comparison study of effectiveness and robustness of control charts for monitoring process mean. Int J Prod Econ 135(1):479–490

    Article  Google Scholar 

  26. Taguchi G, Wu Y (1980) Introduction to off-line quality control. Central Japan Quality Control Association, Nagoya, pp. 49–67

    Google Scholar 

  27. Shamsuzzaman M, Khoo M, Haridy S, Alsyouf I (2014) An optimization design of the 3-EWMA scheme for monitoring mean shifts. Int J Adv Manuf Technol 74(5–8):1061–1076

    Article  Google Scholar 

  28. Haridy S, Wu Z, Abhary K, Castagliola P, Shamsuzzaman M (2014) Development of a multiattribute synthetic-np chart. J Stat Comput Simul 84(9):1884–1903

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Salah Haridy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haridy, S., Maged, A., Kaytbay, S. et al. Effect of sample size on the performance of Shewhart control charts. Int J Adv Manuf Technol 90, 1177–1185 (2017). https://doi.org/10.1007/s00170-016-9412-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9412-8

Keywords

Navigation