Heliyon (Apr 2020)

The optimization of overall equipment effectiveness factors in a pharmaceutical company

  • Okpala Charles Chikwendu,
  • Anozie Stephen Chima,
  • Mgbemena Chika Edith

Journal volume & issue
Vol. 6, no. 4
p. e03796

Abstract

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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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