On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data

Nurudeen Ayobami Ajadi; Saddam Adams Damisa; Osebekwin Ebenezer Asiribo; Ganiyu Abayomi Dawodu

doi:doi:10.11648/j.bsi.20170204.12

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On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data

Nurudeen Ayobami Ajadi, Saddam Adams Damisa, Osebekwin Ebenezer Asiribo, Ganiyu Abayomi Dawodu

Published in Biomedical Statistics and Informatics (Volume 2, Issue 4)

Received: 27 July 2017 Accepted: 16 August 2017 Published: 21 September 2017

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Abstract

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.

Published in	Biomedical Statistics and Informatics (Volume 2, Issue 4)
DOI	10.11648/j.bsi.20170204.12
Page(s)	138-144
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Cusum Chart, Ewma Chart, Average Run length, Mec Chart, Mech Chart, Univariate Control Charts

References

[1]	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.
[2]	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.
[3]	Abbas, N. (2015). Progressive mean as a special case of Exponentially Weighted Moving Average. Quality and Reliability Engineering International. 31: 719–720.
[4]	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.
[5]	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
[6]	Edokpa, I. W., Ikpotokin, O., and Erimafa, J. T. (2009). Journal of mathematical sciences, International centre for advance studies, west bengal, 20: 171-179
[7]	Page, E. S. (1954) Continuous Inspection Schemes. Biometrika, 41, 100–115.
[8]	Roberts, S. W. (1959) Control Chart Tests Based on Geometric Moving Averages. Technometrics, 1, 239–250.
[9]	Shewhart W. (1931). Economic Control of Quality Manufactured Product, D. Van Nostrand, New York; reprinted by the American Society for Quality Control in 1980.
[10]	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.

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APA Style

Nurudeen Ayobami Ajadi, Saddam Adams Damisa, Osebekwin Ebenezer Asiribo, Ganiyu Abayomi Dawodu. (2017). On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data. Biomedical Statistics and Informatics, 2(4), 138-144. https://doi.org/10.11648/j.bsi.20170204.12

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ACS Style

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. Biomed. Stat. Inform. 2017, 2(4), 138-144. doi: 10.11648/j.bsi.20170204.12

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AMA Style

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. Biomed Stat Inform. 2017;2(4):138-144. doi: 10.11648/j.bsi.20170204.12

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@article{10.11648/j.bsi.20170204.12,
  author = {Nurudeen Ayobami Ajadi and Saddam Adams Damisa and Osebekwin Ebenezer Asiribo and Ganiyu Abayomi Dawodu},
  title = {On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {4},
  pages = {138-144},
  doi = {10.11648/j.bsi.20170204.12},
  url = {https://doi.org/10.11648/j.bsi.20170204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170204.12},
  abstract = {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.},
 year = {2017}
}

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TY  - JOUR
T1  - On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data
AU  - Nurudeen Ayobami Ajadi
AU  - Saddam Adams Damisa
AU  - Osebekwin Ebenezer Asiribo
AU  - Ganiyu Abayomi Dawodu
Y1  - 2017/09/21
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170204.12
DO  - 10.11648/j.bsi.20170204.12
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 138
EP  - 144
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170204.12
AB  - 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.
VL  - 2
IS  - 4
ER  -

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Author Information

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

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APA Style

Nurudeen Ayobami Ajadi, Saddam Adams Damisa, Osebekwin Ebenezer Asiribo, Ganiyu Abayomi Dawodu. (2017). On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data. Biomedical Statistics and Informatics, 2(4), 138-144. https://doi.org/10.11648/j.bsi.20170204.12

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ACS Style

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. Biomed. Stat. Inform. 2017, 2(4), 138-144. doi: 10.11648/j.bsi.20170204.12

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AMA Style

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. Biomed Stat Inform. 2017;2(4):138-144. doi: 10.11648/j.bsi.20170204.12

Copy | Download

@article{10.11648/j.bsi.20170204.12,
  author = {Nurudeen Ayobami Ajadi and Saddam Adams Damisa and Osebekwin Ebenezer Asiribo and Ganiyu Abayomi Dawodu},
  title = {On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {4},
  pages = {138-144},
  doi = {10.11648/j.bsi.20170204.12},
  url = {https://doi.org/10.11648/j.bsi.20170204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170204.12},
  abstract = {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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - On Efficient Memory-Type Control Charts for Monitoring out of Control Signals in a Process Using Diabetic Data
AU  - Nurudeen Ayobami Ajadi
AU  - Saddam Adams Damisa
AU  - Osebekwin Ebenezer Asiribo
AU  - Ganiyu Abayomi Dawodu
Y1  - 2017/09/21
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170204.12
DO  - 10.11648/j.bsi.20170204.12
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 138
EP  - 144
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170204.12
AB  - 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.
VL  - 2
IS  - 4
ER  -

Copy | Download