Improvement model to optimize packing times in a Peruvian SME agricultural export company using cellular manufacturing, SMED and standard work

Descripción del Articulo

The agricultural export business in Peru has grown considerably in recent years, playing a key role in the development of the nation’s economy. To adapt to a growing market, companies must optimize their processes not to lose competitive edge. Problems such as excessive production times, low ef-fici...

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Detalles Bibliográficos
Autores: López Morales, Rafael Gabriel, Ocampo Leyva, Edward
Formato: tesis de grado
Fecha de Publicación:2025
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/22978
Enlace del recurso:https://hdl.handle.net/20.500.12724/22978
Nivel de acceso:acceso abierto
Materia:Pendiente
https://purl.org/pe-repo/ocde/ford#2.11.04
Descripción
Sumario:The agricultural export business in Peru has grown considerably in recent years, playing a key role in the development of the nation’s economy. To adapt to a growing market, companies must optimize their processes not to lose competitive edge. Problems such as excessive production times, low ef-ficiency, and loss of the fruit due to rot are frequently met, these liabilities originate from, among some other causes, a high cycle time for the packing process, which can lead to negative economic impact and a worsening of the company’s image. In this research, an improvement model using Lean manu-facturing tools namely Cellular Manufacturing, SMED and Standard Work is proposed. The objective of the model is to optimize packing times, seeking to reduce the duration of the time needed to dispatch a crate of mangoes to at least 10.20 minutes, which following the implementation of the improvement model obtained a value of 9.84 minutes. Additional improvements were achieved such as a 2.19-minute reduction for setup times, 11.9% reduction in the setup time ratio, and 8.9% reduction in moving times. The results obtained, validated by a simulation run in a controlled environment in Python 3.10, proved the effectiveness of the proposed model.
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