Skip to main content
SpringerLink
Log in

Monitoring process variability using exponentially weighted moving sample variance control charts

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

A Publisher's Erratum to this article was published on 18 January 2008

Abstract

Exponentially weighted moving average (EWMA) control charts are regarded as one of the most convenient tools in detecting small process shifts. Although EWMA control charts have been extensively used to monitor the mean of quality characteristics, there are few studies concentrating on the monitoring of process variability by using weighted moving control charts. In this paper, we propose an exponentially weighted moving sample variance (EWMSV) control chart for monitoring process variability when the sample size is equal to 1. The results are compared numerically with other similar methods using the average run length (ARL). Through an example, the practical considerations are presented to implement EWMSV control charts.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Amin RW, Wolff H, Besenfelder W, Baxley R (1999) EWMA control charts for the smallest and largest observations. J Qual Technol 31:189–206

    Google Scholar 

  2. Box GEP (1954) Some theorems on quadratic forms applied in the study of analysis of variance problems, I: effect of inequality of variance in the one-way classification. Ann Math Stat 25:290–302

    Article  MATH  MathSciNet  Google Scholar 

  3. Castagliola P (2005) A new S2-EWMA control chart for monitoring the process variance. Qual Reliab Eng Int 21(8):781–794

    Article  Google Scholar 

  4. Chen G, Cheng SW, Xie H (2001) Monitoring process mean and variability with one EWMA chart. J Qual Technol 33:223–233

    Google Scholar 

  5. Crowder SV (1987) A simple method for studying run-length distributions of exponentially weighted moving average charts. Technometrics 29:401–407

    Article  MATH  MathSciNet  Google Scholar 

  6. Crowder SV (1989) Design of exponentially weighted moving average schemes. J Qual Technol 21:155–162

    Google Scholar 

  7. Crowder SV, Hamilton M (1992) An EWMA for monitoring a process standard deviation. J Qual Technol 24:12–21

    Google Scholar 

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

    Article  MATH  Google Scholar 

  9. Gan FF (1995) Joint monitoring of process mean and variance using exponentially weighted moving average control charts. Technometrics 37:446–453

    Article  MATH  Google Scholar 

  10. Hunter JS (1986) The exponentially weighted moving average. J Qual Technol 18:203–210

    Google Scholar 

  11. Lucas JM, Saccucci MS (1990) Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32:1–12

    Article  MathSciNet  Google Scholar 

  12. MacGregor JF, Harris TJ (1993) The exponentially weighted moving variance. J Qual Technol 25:106–118

    Google Scholar 

  13. Montgomery DC (2005) Introduction to statistical quality control, 5th edn. Wiley, New York

    MATH  Google Scholar 

  14. Ng CH, Case KE (1989) Development and evaluation of control charts using exponentially weighted moving averages. J Qual Technol 21:242–250

    Google Scholar 

  15. Quesenberry CP (1993) The effect of sample size on estimated limits for X and X control charts. J Qual Technol 25:237–247

    Google Scholar 

  16. Roberts SW (1959) Control chart tests based on geometric moving averages. Technometrics 1:239–250

    Article  Google Scholar 

  17. Robinson PB, Ho TY (1978) Average run lengths of geometric moving average charts by numerical methods. Technometrics 20(1):85–93

    Article  MATH  Google Scholar 

  18. Sweet AL (1986) Control charts using coupled exponentially weighted moving averages. IIE Trans 18:26–33

    Article  Google Scholar 

  19. Wortham AW, Ringer LJ (1971) Control via exponential smoothing. Transport Logist Rev 7:33–39

    Google Scholar 

  20. Wortham AW (1972) The use of exponentially smoothed data in continuous process control. Int J Prod Res 10:393–400

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Department, Iran University of Science and Technology, Tehran, 16844, Iran

    Majid Eyvazian, S. G. Jalali Naini & A. Vaghefi

Authors
  1. Majid Eyvazian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. G. Jalali Naini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Vaghefi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Vaghefi.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s00170-007-1359-3

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eyvazian, M., Jalali Naini, S.G. & Vaghefi, A. Monitoring process variability using exponentially weighted moving sample variance control charts. Int J Adv Manuf Technol 39, 261–270 (2008). https://doi.org/10.1007/s00170-007-1213-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-007-1213-7

Keywords

Search

Navigation