Developing a maintenance management model for service level optimization: a case study in peruvian heavy goods transport SMES

• Autores: Valverde Gonzales, Alvaro Odon, Salazar Ccaccya, Danny Daniel
• Formato: tesis de grado
• Fecha de Publicación: 2025
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/23530
• Enlace del recurso: https://hdl.handle.net/20.500.12724/23530
• Nivel de acceso: acceso abierto
• Materia: Pendiente; https://purl.org/pe-repo/ocde/ford#2.11.04

The study addressed critical inefficiencies in heavy goods transport SMEs, focusing on improving service levels and operational reliability. It identified challenges such as low On-Time In Full (OTIF) rates and high equipment failure rates, which significantly impacted service quality. The proposed research integrated Total Productive Maintenance (TPM) principles and Process Management into a unified maintenance model, emphasizing planned and autonomous maintenance strategies. Key findings revealed measurable improvements: OTIF increased by 7.24%, fuel shortages dropped by 50%, and damaged load indices decreased by 23.20%. These outcomes underscored the model’s potential to enhance resource utilization and service reliability. This research demonstrated the relevance of systematic maintenance strategies in aligning operational activities with organizational goals, fostering efficiency and sustainability. By addressing specific gaps in existing maintenance practices, the study provided a replicable framework applicable across similar contexts, offering actionable insights for enhancing operational metrics in resource-constrained environments. It significantly contributed to bridging knowledge gaps in maintenance management for SMEs, particularly in the transport sector. Future investigations should explore integrating advanced digital tools, such as IoT-based monitoring systems, to expand predictive maintenance capabilities. Moreover, cross-sectoral comparative analyses could enrich understanding of the model’s adaptability and limitations. This research sets the groundwork for ongoing advancements in SME maintenance management.