Abstract
Statistical Process Control (SPC) is widely used in practice, and the control chart is a main tool in SPC. The control chart monitors process quality and detects process variations in real time to reduce the defective products. Regulatory maps are used in clinical trials and agronomic industries, and the mean and variance are not constants, and the coefficient of variation is an indicator of the reliability of chemical tests. However, the coefficient of variation control chart (CV Chart) monitors the coefficient of variation, and the ability to detect small shift sizes is not good. This study uses a double sampling plan combined with a coefficient of variation control chart (DS CV chart) to improve the performance of CV Chart. A design model of DS CV chart is also created to optimize its parameters. The results of the study show that the good performance of DS CV chart in small shift detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, M.B., Kairouani, L.: Multi-objective optimization of operating parameters of a MSF-BR desalination plant using solver optimization tool of Matlab software. Desalination 381, 71–83 (2016)
Ascione, F., Bianco, N., Mauro, G.M., Napolitano, D.F.: Building envelope design: multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones. Energy 174, 359–374 (2019)
Connett, J.E., Lee, W.W.: Estimation of the coefficient of variation from laboratory analysis of split specimens for quality control in clinical trials. Control. Clin. Trials 11, 24–36 (1990)
Daudin, J.: Double sampling Xbar charts. J. Qual. Technol. 24, 78–87 (1992)
He, D., Grigoryan, A.: Construction of double sampling s-control charts for agile manufacturing. Qual. Reliab. Eng. Int. 18, 343–355 (2002)
He, D., Grigoryan, A.: An improved double sampling s chart. Int. J. Prod. Res. 41, 2663–2679 (2003)
He, D., Grigoryan, A., Sigh, M.: Design of double-and triple-sampling X-bar control charts using genetic algorithms. Int. J. Prod. Res. 40, 1387–1404 (2002)
Iglewicz, B., Myers, R.H.: Comparisons of approximations to the percentage points of the sample coefficient of variation. Technometrics 12, 166–169 (1970)
Irianto, D., Shinozaki, N.: An optimal double sampling Xbar control chart. Int. J. Ind. Eng. 5, 226–234 (1998)
Kang, C.W., Lee, M.S., Seong, Y.J., Hawkins, D.M.: A control chart for the coefficient of variation. J. Qual. Technol. 39, 151–158 (2007)
Lee, P.H.: Joint statistical design of Xbar and s charts with combined double sampling and variable sampling interval. Eur. J. Oper. Res. 225, 285–297 (2013)
Razaa, S.A., Abdullakutty, F.C., Rathinam, S., Govindaluri, S.M.: Multi-objective framework for process mean selection and price differentiation with leakage effects under price-dependent stochastic demand. Comput. Ind. Eng. 172, 698–708 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Torng, C., Jhong, H. (2020). A Design and Evaluation of Coefficient of Variation Control Chart. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-60700-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-60700-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60699-2
Online ISBN: 978-3-030-60700-5
eBook Packages: Computer ScienceComputer Science (R0)