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Sustainable Development Goals
09 Indústria, inovação e infraestruturaCollections
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IMPLEMENTATION OF THE LEAD TIME METHODOLOGY FOR MANAGING THE ANALYSIS OF PHARMACEUTICAL INPUTS IN QUALITY CONTROL
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Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos). Rio de Janeiro, RJ, Brasil.
Abstract
Introduction: The search for high vaccination coverage has become a concern in a global scenario following the Covid-19 pandemic. In an industrial environment, it is necessary to increase the production capacity of vaccines, meeting all the quality and product safety parameters set out in Good Manufacturing Practices. Quality control directly impacts the release stage and, consequently, the fulfillment of deliveries to the end customer. The term Lead Time is part of the concept of Lean Manufacturing and can help monitor process times to reduce time and generate optimization. Objectives: Evaluate the effectiveness of implementing the Lead Time tool in the Quality Control area. Methodology: The tool mapped process deficiencies, whether internal or external, which caused delays. After classifying the causes of delays into the six categories used in the Ishikawa Diagram (Method,[ Raw Material, Labor, Machines, Measurement, and Environment), action plans were generated. Data was used for inputs A, B, and C over seven years, with the sixth year as the starting point for monitoring. Descriptive time analysis and the Kruskal-Wallis non-parametric hypothesis test were used to compare the periods before and after the application of the tool, at a 5% significance level. The hypothesis test was not applied to evaluate the difference between the input groups due to a different flow of analyses. Quality Control used Minitab® 21.4.2 software to conduct the analyses. Results: The tool reduced median release time by 75.6% when comparing the first five periods to the last two periods, with all inputs considered. For Input A, the units analyzed showed a 76.5% reduction in the median time, with 28 units. Input B, there was a 74.2% reduction based on the 21 analyzed units. Input C recorded reduce 57.1% in the 11 analyzed units. The non-parametric test showed significant differences in the median times for inputs A (p-value 0.001) and B (p-value 0.000). Test for input C was not possible due to the small sample size. Conclusion: The data shows a significant improvement in release times, which emphasizes the effectiveness of the management tool, leading to a reduction in the risk of shortages of vaccine resources. However, it is crucial to note that observing and mapping discrepant results does not optimize processes and that addressing gaps parallel to the end process requires effective action.
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