Application of Lean-Total Productive Maintenance tools to reduce setup times and machine stoppages on the molding line of an SME in the food industry

• Autores: Peralta Pereda, Renzo Mauricio, Zamora Ccahuantico, Frank Willy
• Formato: tesis de grado
• Fecha de Publicación: 2022
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/19009
• Enlace del recurso: https://hdl.handle.net/20.500.12724/19009
• Nivel de acceso: acceso abierto
• Materia: Mantenimiento productivo total; Producción eficiente; Empleo del tiempo; Procesos de manufactura; Pequeñas y medianas empresas; Industria alimentaria; Total productive maintenance; Lean manufacturing; Time management; Manufacturing processes; Small business; Food industry and trade; https://purl.org/pe-repo/ocde/ford#2.11.04

The demand for the food industry has maintained sustained growth in recent years. In Peru, as a consequence of the high competitiveness in the food sector, it has reflected a considerable contribution to the manufacturing GDP with 20% and 2.6% to the national GDP (Gross domestic product). However, the high demands of the market are not satisfied due to problems such as the availability of machinery, mainly due to high programming times, and high frequency of machine stoppages, among others. These problems generate large monetary losses and a bad image in front of large potential customers. Thus, this research work, taking as a case study an SME (Small and Medium-Sized companies) of the food sector, seeks to provide the application of the tools of the LM (Lean Manufacturing) and TPM (Total Productive Maintenance) philosophies, to reduce the high setup times and the high frequency of machine stoppages that currently harm the company and generate an impact of 3.95% in operational costs. The model was validated using the Arena simulator, where a 30.83% reduction in machine programming time and a reduction in machine stop frequencies were obtained, which led to an improvement in MTTR (Mean Time to Repair) indicators with a 21.60% reduction and a 4.66% increase in MTBF (Mean Time Between Failures). This new integration of engineering tools makes it possible to solve the main problems faced by large companies in the sector to meet market demands and adapt to new needs.