Improvement in Delivery Times Using Lean Manufacturing Tools in a SME the Beverage Sector in Peru

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The non-alcoholic beverage industry, such as bottled water, is one of the largest industries in which the process is carried out at the lowest cost, but with the highest quality in the final product. This sector has a significant impact on the world economy, and consumption per person is constantly...

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Detalles Bibliográficos
Autores: Flores Pérez, Alberto Enrique, Hernandez Asian, Aisha Anahi, Tello Cornejo, Antonella Paola
Formato: objeto de conferencia
Fecha de Publicación:2023
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/19373
Enlace del recurso:https://hdl.handle.net/20.500.12724/19373
https://doi.org/10.3233/ATDE230097
Nivel de acceso:acceso abierto
Materia:Entrega de mercancías
Bebidas sin alcohol
Agua
Producción eficiente
Delivery of goods
Non-alcoholic beverages
Water
Lean manufacturing
https://purl.org/pe-repo/ocde/ford#2.11.04
Descripción
Sumario:The non-alcoholic beverage industry, such as bottled water, is one of the largest industries in which the process is carried out at the lowest cost, but with the highest quality in the final product. This sector has a significant impact on the world economy, and consumption per person is constantly growing. This research focuses on the improvement of delivery times through Lean Manufacturing tools. The model makes use of tools such as 5S' to create and maintain a more efficient and productive space, improve overall equipment efficiency through Total Productive Maintenance, and optimize material and operator movements by eliminating unnecessary ones using Standard Work, from that were positive indicators for management. For the validation of our proposal, an integrating model of the pilot plans was carried out in order to corroborate the efficiency of the proposed tools using the Arena software. By validating the proposed model, it was possible to reduce the rate of products delivered out of time by 37.82%, increase the OEE of the machine by 16% and reduce cycle times.
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