Application of lean and TPM tools to improve OEE: a case study in peruvian metalworking SMES

Descripción del Articulo

The research in circumvention focused on systematic problems largely prevalent among small and medium sized metalworking enterprises (SMEs) in Peru which is an important area in terms of both employment growth and competitiveness. The existing literature pointed out to a number of structural variabi...

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Detalles Bibliográficos
Autor: Pinto Zegarra, Diego Rene
Formato: tesis de grado
Fecha de Publicación:2025
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/22681
Enlace del recurso:https://hdl.handle.net/20.500.12724/22681
Nivel de acceso:acceso abierto
Materia:Producción eficiente
Mantenimiento productivo total
Industria metalmecánica
Pequeñas y medianas empresas
Proceso de mejora continua
Estudio de casos
https://purl.org/pe-repo/ocde/ford#2.11.04
Descripción
Sumario:The research in circumvention focused on systematic problems largely prevalent among small and medium sized metalworking enterprises (SMEs) in Peru which is an important area in terms of both employment growth and competitiveness. The existing literature pointed out to a number of structural variability as well as lack of sufficient quality inspections to have negative productivity effects. In this study, the aim was enhancement of Overall Equipment Effectiveness (OEE) using integrated production comprising Lean Manufacturing and Total Productive Maintenance (TPM). Among the fundamentals of the work were standardization of work, autonomous and preventive maintenance of equipment. Results showed OEE when measured on the first production model increased from 64.93% to 81.15% and for the second model it increased while cycle time decreased from 50.69 to 43.87 hours implying an improvement of 13.46 percent. This study is relevant to the field of operational management as it provides a functional model that improves the reliability of outputs and the efficiency of inputs. The results point to the need of sustaining improvement and involving employees. Future research should seek to assess the relevance of this model in other areas and include digital components to enhance predictive maintenance.
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