Abstract
Control chart is the most important Statistical Process Control tool used to monitor reliability and performance of industrial processes. For monitoring process dispersion, \(R\) and \(S\) charts are widely used. These control charts perform better under the ideal assumption of normality but are well known to be very inefficient in presence of outliers or departures from normality. In this study we propose a new control chart for monitoring process dispersion, namely the \(D\) chart, and compared its performance with \(R\) and \(S\) charts using probability to signal as a performance measure. It has been observed that the newly proposed chart is superior to \(R\) chart and is a close competitor to S chart under normality of quality characteristic. When the assumption of normality is violated, \(D\) chart is more powerful than both \(R\) and \(S\) charts. This study will help quality practitioners to choose an efficient and robust alternative to \(R\) and \(S\) charts for monitoring dispersion of industrial processes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbasi SA, Miller A (2011) D chart: an efficient alternative to monitor process dispersion. Lecture notes in engineering and computer science. In: Proceedings of the world congress on engineering and computer science (2011) Vol II, WCECS 2011, 19–21 October, 2011. San Francisco, USA, pp 933–938
Abu-Shawiesh MO, Abdullah MB (2000) Estimating the process standard deviation based on downton’s estimator. Qual Eng 12(3):357–363
Barnett FC, Mullen K, Saw JG (1967) Linear estimates of a population scale parameter. Biometrika 54:551–554
Bissell D (1994) Statistical methods for SPC and TQM. Chapman& Hall, New York
Downton F (1966) Linear estimates with polynomial coefficients. Biometrika 53(1):129–141
Gonzalez IM, Viles E (2001) Design of R control chart assuming a gamma distribution. Econ Qual Control 16:199–204
Hwang SL, Lin JT, Liang GF, Yau YJ, Yenn TC, Hsu CC (2008) Application control chart concepts of designing a pre-alarm system in the nuclear power plant control room. Nucl Eng Des 238(12):3522–3527
James PC (1989) \(\text{ C}_pk\) equivalencies. Quality 28(9):75
Levinson WA, Polny A (1999) Spc for tool particle counts. Semicond Int 22(6):117–121
Masson P (2007) Quality control techniques for routine analysis with liquid chromatography in laboratories. J Chromatogr A 1158(1–2):168–173
Montgomery DC (2001) Introduction to statistical quality control. Wiley, New York
Ramalhoto MF, Morais M (1999) Shewhart control charts for the scale parameter of a weibull control variable with fixed and variable sampling intervals. Journal of Applied Statistics 26(1):1129–160
Ryan PR (2000) Statistical methods for quality improvement. Wiley, New York
Shewhart WA (1931) Economic control of quality manufactured product. In: Van Nostrand D (ed) Reprinted by the american society for quality control in 1980. Milwauker, New York
Wang Z, Liang R (2008) Discuss on applying spc to quality management in university education. In: Proceedings of the 9th international conference for young computer scientists. ICYCS 2008, pp 2372–2375
Woodall WH (2006) The use of control charts in health-care and public-health surveillance. J Qual Technol 38(2):89–104
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Abbasi, S.A., Miller, A. (2013). An Efficient Dispersion Control Chart. In: Kim, H., Ao, SI., Rieger, B. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 170. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4786-9_5
Download citation
DOI: https://doi.org/10.1007/978-94-007-4786-9_5
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4785-2
Online ISBN: 978-94-007-4786-9
eBook Packages: EngineeringEngineering (R0)