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The optimization of overall equipment effectiveness factors in a pharmaceutical company

Over the years, the inability of pharmaceutical companies to achieve optimum maintenance of their equipment, and enhanced machine availability for better resource and maintenance utilization, has adversely affected their competitive advantage. The need to adopt a unique production technique that wil...

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Autores principales: Chikwendu, Okpala Charles, Chima, Anozie Stephen, Edith, Mgbemena Chika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170996/
https://www.ncbi.nlm.nih.gov/pubmed/32322743
http://dx.doi.org/10.1016/j.heliyon.2020.e03796
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author Chikwendu, Okpala Charles
Chima, Anozie Stephen
Edith, Mgbemena Chika
author_facet Chikwendu, Okpala Charles
Chima, Anozie Stephen
Edith, Mgbemena Chika
author_sort Chikwendu, Okpala Charles
collection PubMed
description Over the years, the inability of pharmaceutical companies to achieve optimum maintenance of their equipment, and enhanced machine availability for better resource and maintenance utilization, has adversely affected their competitive advantage. The need to adopt a unique production technique that will curb their numerous equipment maintenance challenges, and also re-position them for world-class manufacturing will not only reduce their losses, but will also increase their throughput and profitability. To address a Pharmaceutical firm's maintenance challenges, data were obtained for the Overall Equipment Effectiveness (OEE) factors after Total Productive Maintenance (TPM) implementation in the company. Minitab 16.0 software was used to analyze the data collected, and the results showed that the highest value of 98.90 and 96.39 in the descriptive statistics for the maximum and mean respectively, underscore the importance of quality in the company's products. The percentage of mean for quality, availability, and performance obtained were 96.3906, 60.4938, and 27.6188 respectively. This once again showed that quality of products is the greatest OEE factor that pharmaceutical companies must take seriously in order to reduce the six big losses in their manufacturing processes. Response Surface Method (RSM), with the application of Design Expert software with Box-Behnken as the design type was used to model, analyze, and optimize the Overall Equipment Effectiveness (OEE) using availability, quality, and performance as the input parameters. The analysis of both the actual and coded values, which is the main contribution of the study showed that quality has the greatest value followed by availability and performance. Also, OEE must be set at 10.3 and 629.5 for both the lower and upper limits respectively, in order to effectively reduce downtime, setup cost, the inherent wastes, as well as the six big losses.
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spelling pubmed-71709962020-04-22 The optimization of overall equipment effectiveness factors in a pharmaceutical company Chikwendu, Okpala Charles Chima, Anozie Stephen Edith, Mgbemena Chika Heliyon Article Over the years, the inability of pharmaceutical companies to achieve optimum maintenance of their equipment, and enhanced machine availability for better resource and maintenance utilization, has adversely affected their competitive advantage. The need to adopt a unique production technique that will curb their numerous equipment maintenance challenges, and also re-position them for world-class manufacturing will not only reduce their losses, but will also increase their throughput and profitability. To address a Pharmaceutical firm's maintenance challenges, data were obtained for the Overall Equipment Effectiveness (OEE) factors after Total Productive Maintenance (TPM) implementation in the company. Minitab 16.0 software was used to analyze the data collected, and the results showed that the highest value of 98.90 and 96.39 in the descriptive statistics for the maximum and mean respectively, underscore the importance of quality in the company's products. The percentage of mean for quality, availability, and performance obtained were 96.3906, 60.4938, and 27.6188 respectively. This once again showed that quality of products is the greatest OEE factor that pharmaceutical companies must take seriously in order to reduce the six big losses in their manufacturing processes. Response Surface Method (RSM), with the application of Design Expert software with Box-Behnken as the design type was used to model, analyze, and optimize the Overall Equipment Effectiveness (OEE) using availability, quality, and performance as the input parameters. The analysis of both the actual and coded values, which is the main contribution of the study showed that quality has the greatest value followed by availability and performance. Also, OEE must be set at 10.3 and 629.5 for both the lower and upper limits respectively, in order to effectively reduce downtime, setup cost, the inherent wastes, as well as the six big losses. Elsevier 2020-04-18 /pmc/articles/PMC7170996/ /pubmed/32322743 http://dx.doi.org/10.1016/j.heliyon.2020.e03796 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Chikwendu, Okpala Charles
Chima, Anozie Stephen
Edith, Mgbemena Chika
The optimization of overall equipment effectiveness factors in a pharmaceutical company
title The optimization of overall equipment effectiveness factors in a pharmaceutical company
title_full The optimization of overall equipment effectiveness factors in a pharmaceutical company
title_fullStr The optimization of overall equipment effectiveness factors in a pharmaceutical company
title_full_unstemmed The optimization of overall equipment effectiveness factors in a pharmaceutical company
title_short The optimization of overall equipment effectiveness factors in a pharmaceutical company
title_sort optimization of overall equipment effectiveness factors in a pharmaceutical company
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170996/
https://www.ncbi.nlm.nih.gov/pubmed/32322743
http://dx.doi.org/10.1016/j.heliyon.2020.e03796
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