Case of Preferential Selection of Attribute over Variable Control Charts in Trend Analysis of Microbiological Count in Water

Mostafa Essam Eissa, Engy Refaat Rashed & Dalia Essam Eissa

doi:https://doi.org/10.29329/actanatsci.2023.353.01

Case Report | Acta Natura et Scientia 2023, Vol. 4(1) 1-9

Case of Preferential Selection of Attribute over Variable Control Charts in Trend Analysis of Microbiological Count in Water

Mostafa Essam Eissa, Engy Refaat Rashed & Dalia Essam Eissa

pp. 1 - 9 | DOI: https://doi.org/10.29329/actanatsci.2023.353.01 | Manu. Number: MANU-2211-09-0001.R1

Published online: February 14, 2023 | Number of Views: 86 | Number of Download: 320

Save PDF

Abstract

Monitoring the quality criteria in the healthcare industry and the pharmaceutical field specifically is a crucial mission activity to ensure the delivery of safe and effective treatment to patients with predictable and acceptable medicinal properties. One of the critical ingredients that are found in many activities is water. In the present study, the inspection characteristic trend was monitored by collecting results of the microbial count of Purified Water (PW) at two points in the water treatment station. The dataset was examined for pattern and distribution after processing and stratification and before conducting transformation using Microsoft Excel. Then, control charts were constructed using Statistical Process Control (SPC) software. The results showed that transformation improved data normalization for the Individual-Moving Range (I-MR) chart while the original pattern of the dataset was lost distorted. On the other hand, other advantages could be retained when using the Laney chart where no transformation was implemented on original raw data. The selection should be based on the nature of the process aim and condition.

Keywords: Laney, I-MR, Purified water, Control limits, Statistical process control, Transformation

How to Cite this Article?

APA 6th edition
Eissa, M.E., Rashed, E.R. & Eissa, D.E. (2023). Case of Preferential Selection of Attribute over Variable Control Charts in Trend Analysis of Microbiological Count in Water . Acta Natura et Scientia, 4(1), 1-9. doi: 10.29329/actanatsci.2023.353.01

Harvard
Eissa, M., Rashed, E. and Eissa, D. (2023). Case of Preferential Selection of Attribute over Variable Control Charts in Trend Analysis of Microbiological Count in Water . Acta Natura et Scientia, 4(1), pp. 1-9.

Chicago 16th edition
Eissa, Mostafa Essam, Engy Refaat Rashed and Dalia Essam Eissa (2023). "Case of Preferential Selection of Attribute over Variable Control Charts in Trend Analysis of Microbiological Count in Water ". Acta Natura et Scientia 4 (1):1-9. doi:10.29329/actanatsci.2023.353.01.

References

Bhagwat, V. R. (2019). Safety of water used in food production. In R. L. Singh & S. Mondal (Eds.), Food Safety and Human Health (pp. 219–247). Elsevier. https://doi.org/10.1016/B978-0-12-816333-7.00009-6 [Google Scholar] [Crossref]
Drinking Water and Health. (1982). Drinking Water and Health Volume 4. Safe Drinking Water Committee. National Academies Press. https://doi.org/10.17226/325 [Google Scholar] [Crossref]
Eissa, M. (2015). Shewhart control chart in microbiological quality control of purified water and its use in quantitative risk evaluation. Pharmaceutical and Biosciences Journal, 4(1), 45–51. https://doi.org/10.20510/ukjpb/4/i1/87845 [Google Scholar] [Crossref]
Eissa, M. (2016). Study of microbial distribution from different processing stages in purified water production plant of pharmaceutical manufacturing facility. Research & Reviews: Journal of Microbiology and Virology, 6(1), 31-45. [Google Scholar]
Eissa, M. (2018a). Microbiological quality of purified water assessment using two different trending approaches: A case study. Sumerianz Journal of Scientific Research, 1(3), 75-79. [Google Scholar]
Eissa, M. (2018b). Variable and attribute control charts in trend analysis of active pharmaceutical components: Process efficiency monitoring and comparative study. Experimental Medicine, 1(2), 31-44. https://doi.org/10.31058/j.em.2018.11003 [Google Scholar] [Crossref]
Eissa, M., Rashed, E., & Eissa, D. (2022). Principal component analysis in long term assessment of total viable plate count of municipal water distribution network system in healthcare facility. Environmental Research and Technology, 5(2), 165-171. https://doi.org/10.35208/ert.1062683 [Google Scholar] [Crossref]
Elisson, A. (2017). Implementing SPC for non-normal processes with the I-MR chart: A case study [M.Sc. Thesis. Royal Institute of Technology (KTH)] [Google Scholar]
Essam Eissa, M. (2018). Investigation of microbiological quality of water from the feed source to the terminal application in the healthcare facility: A case study. Health Research, 2(1), 16-23. https://doi.org/10.31058/j.hr.2018.21002 [Google Scholar] [Crossref]
Hamed, M. S. (2017). Multivariate statistical process of Hotelling’s T2 control charts procedures with industrial application. Journal of Statistics: Advances in Theory and Applications, 18(1), 1-44. https://doi.org/10.18642/jsata_7100121868 [Google Scholar] [Crossref]
Held, B. (2018). Microsoft excel functions and formulas (Fourth edition). Mercury Learning and Information. [Google Scholar]
Hubbard, M. R. (2003). Statistical quality control for the food industry. Springer. https://doi.org/10.1007/978-1-4615-0149-7 [Google Scholar] [Crossref]
Jones, G., & Govindaraju, K. (2001). A graphical method for checking attribute control chart assumptions. Quality Engineering, 13(1), 19-26. https://doi.org/10.1080/08982110108918620 [Google Scholar] [Crossref]
Keller, P. A. (2011). Statistical process control demystified. McGraw-Hill. [Google Scholar]
Khakifirooz, M., Tercero-Gómez, V. G., & Woodall, W. H. (2021). The role of the normal distribution in statistical process monitoring. Quality Engineering, 33(3), 497–510. https://doi.org/10.1080/08982112.2021.1909731 [Google Scholar] [Crossref]
Laney, D. B. (2002). Improved control charts for attributes. Quality Engineering, 14(4), 531–537. https://doi.org/10.1081/QEN-120003555 [Google Scholar] [Crossref]
Levine, D. M., Stephan, D., & Szabat, K. A. (2021). Statistics for managers using Microsoft Excel (Ninth edition, global edition). Pearson. [Google Scholar]
Moon, J. (2020). An Investigation into the use of Laney U Chart as a visual schedule tracker to graphically monitor the schedule performance index. Journal of Engineering, Project, and Production Management, 10(1), 35-42. [Google Scholar]
Mosadeghrad, A. M. (2014). Factors influencing healthcare service quality. International Journal of Health Policy and Management, 3(2), 77–89. https://doi.org/10.15171/ijhpm.2014.65 [Google Scholar] [Crossref]
Motschman, T. L., & Moore, S. B. (1999). Corrective and preventive action. Transfusion Science, 21(2), 163-178. https://doi.org/10.1016/S0955-3886(99)00088-0 [Google Scholar] [Crossref]
Newton, I. (Ed.). (2014). Minitab cookbook: Over 110 practical recipes to explore the vast array of statistics in Minitab 17. Packt Publishing. [Google Scholar]
Sardella, M., Belcher, G., Lungu, C., Ignoni, T., Camisa, M., Stenver, D. I., Porcelli, P., D’Antuono, M., Castiglione, N. G., Adams, A., Furlan, G., Grisoni, I., Hall, S., Boga, L., Mancini, V., Ciuca, M., Chonzi, D., Edwards, B., Mangoni, A. A., … Le Louet, H. (2021). Monitoring the manufacturing and quality of medicines: A fundamental task of pharmacovigilance. Therapeutic Advances in Drug Safety, 12, 204209862110384. https://doi.org/10.1177/20420986211038436 [Google Scholar] [Crossref]
Skinner, J. (2018). Statistics for immunologists. Current Protocols in Immunology, 122, e54. https://doi.org/10.1002/cpim.54 [Google Scholar] [Crossref]
Smarter Solutions Inc. (2022). Transforming individuals control chart data and process capability reporting in one chart. Smarter Solutions, Inc. https://smartersolutions.com/resources/transforming-individuals-control-chart-data/ [Google Scholar]
Triola, M. (2014). Minitab manual. Pearson Education. [Google Scholar]
Wheeler, D. (2014, February 26). Myths about process behavior charts. Quality Digest. http://www.qualitydigest.com/inside/quality-insider-article/myths-about-process-behavior-charts-090711.html [Google Scholar]

Volume 4 Issue 1

June 2023

All Articles