On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data
Biomedical Statistics and Informatics
Volume 2, Issue 4, December 2017, Pages: 138-144
Received: Jul. 27, 2017;
Accepted: Aug. 16, 2017;
Published: Sep. 21, 2017
Views 2065 Downloads 170
Nurudeen Ayobami Ajadi, Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
Saddam Adams Damisa, Department of Statistics, Ahmadu Bello University, Zaria, Nigeria
Osebekwin Ebenezer Asiribo, Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
Ganiyu Abayomi Dawodu, Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
Follow on us
Control chart is a useful technique which helps in detecting out of control signal in a process and it can either be a memory-type or memory-less control chart. This work is focused on evaluating the monthly incidence of diabetic disease using four univariate memory-type control charts. In this study we evaluated the average run length (ARL) properties of the memory-type control charts by adjusting the ARL value’s determinant parameters in each control charts, and the ARL value was set to be 500. The output of the analysis of the data set indicates that the exponential weighted moving average (EWMA) control chart is better in detecting the out of control signal faster in small shifts than its counter parts including CUSUM, MECH and MEC charts. This will help in having adequate plans and prevent the increase in diabetics in the country.
Cusum Chart, Ewma Chart, Average Run length, Mec Chart, Mech Chart, Univariate Control Charts
To cite this article
Nurudeen Ayobami Ajadi,
Saddam Adams Damisa,
Osebekwin Ebenezer Asiribo,
Ganiyu Abayomi Dawodu,
On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data, Biomedical Statistics and Informatics.
Vol. 2, No. 4,
2017, pp. 138-144.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abbas, N., Riaz, M., and Does, R. J. M M. (2013). Mixed Exponentially Weighted Moving Average – Cumulative Sum charts for Process Monitoring. Quality and Reliability Engineering International, 29(3), 345 – 356.
Abbas, N., Zafar, R. F. Riaz, M, and Hussain Z. (2013). Progressive mean control chart for monitoring location parameter. Quality and Reliability Engineering International. 29(3): 357–367.
Abbas, N. (2015). Progressive mean as a special case of Exponentially Weighted Moving Average. Quality and Reliability Engineering International. 31: 719–720.
Ajadi J. O., Riaz M., Al-Ghamdi K. (2016) “On Increasing the sensitivity of Mixed EWMA-CUSUM Control Charts for Location Parameter”, Journal of Applied Statistics, 43(7), 1262-1278.
Ajadi, J. O and Riaz, M (2016) “Mixed Multivariate EWMA-CUSUM for Improved Process Monitoring”, Communications in Statistics-Theory and Methods; available online at: http://dx.doi.org/10.1080/03610926.2016.1139132
Edokpa, I. W., Ikpotokin, O., and Erimafa, J. T. (2009). Journal of mathematical sciences, International centre for advance studies, west bengal, 20: 171-179
Page, E. S. (1954) Continuous Inspection Schemes. Biometrika, 41, 100–115.
Roberts, S. W. (1959) Control Chart Tests Based on Geometric Moving Averages. Technometrics, 1, 239–250.
Shewhart W. (1931). Economic Control of Quality Manufactured Product, D. Van Nostrand, New York; reprinted by the American Society for Quality Control in 1980.
Zaman, B., Riaz, M., Abbas, N. and Does, R. J. M. M. (2014). Mixed CUSUM-EWMA Control Charts: An Efficient Way of Monitoring Process Location. Quality and Reliability Engineering International, DOI: 10.1002/qre.1678.