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Improvement of the Production Process, Through Simulation Models, in a Pet Bottle Manufacturing Company

  • Universidad Nacional Mayor de San Marcos

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This research work develops an analysis based on the results of the simulation of the production flow of a PET plastic bottle factory in San Juan de Lurigancho, Lima–Peru, using data collection techniques through the review of records and direct observation. Likewise, data processing techniques were used through the application of the statistical software StatFit, which was processed and simulated by the ProModel software. The objective is to generate a proposal to improve the production flow of the companies in this area, based on the results obtained in the case study model. Among the results, it was found that the reorganization of the workers’ functions is more efficient, and by improving the percentage of machine utilization, the incidence of bottlenecks is reduced. The results were validated by applying the statistical test called tests of means. Finally, the findings showed that it is possible to improve time in the production flow. It was also found that it is not necessary to hire or dismiss someone for improvement, only a better organization of the points involved in the process was needed.

Idioma originalInglés
Título de la publicación alojadaIntelligent Sustainable Systems - Selected Papers of WorldS4 2022
EditoresAtulya K. Nagar, Dharm Singh Jat, Durgesh Kumar Mishra, Amit Joshi
Páginas105-118
Número de páginas14
DOI
EstadoPublicada - 2023
Evento6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022 - London, Reino Unido
Duración: 24 ago. 202227 ago. 2022

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen578
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022
País/TerritorioReino Unido
CiudadLondon
Período24/08/2227/08/22

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