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

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.
Cite This Article
  • 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
  • Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria

  • Department of Statistics, Ahmadu Bello University, Zaria, Nigeria

  • Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria

  • Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria

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