Sistema integrado de gestión administrativa y su influencia en los procesos logísticos de la gerencia subregional de Ucayali 2022

• Autor: Vargas Flores, Boris Fernando
• Formato: tesis de maestría
• Fecha de Publicación: 2023
• Institución: Universidad Nacional De La Amazonía Peruana
• Repositorio: UNAPIquitos-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.unapiquitos.edu.pe:20.500.12737/9826
• Enlace del recurso: https://hdl.handle.net/20.500.12737/9826
• Nivel de acceso: acceso abierto
• Materia: Sistemas de información para la gestión; Logística; Adquisiciones; Gestión de almacenes; Tecnología de la información; https://purl.org/pe-repo/ocde/ford#5.02.04; https://purl.org/pe-repo/ocde/ford#5.06.02

The objective of this research was to determine the influence of the integrated administrative management system in the logistics processes of the Subregional Management of Ucayali 2022; opting for an applied type study, correlational descriptive level and non-experimental design, the same one that has selected a sample of 93 workers of said institution. For data collection, I used the survey as a technique and the questionnaire as an instrument, therefore, the processing was through the Excel program and SPSS v26. The investigation concludes that the integrated administrative management system (SIGA) is efficient (38%). On the other hand, it has been shown that the logistics process is adequately developed (40%). The integrated administrative management system predicts the behavior of the logistics process, since it explains 34.7% of this change over other variables that were not measured, therefore, it is identified that the influence of the predictors is significant at the 0.000 level. Likewise, the logistics module predicts the behavior of the logistics process, since it explains 29.9% of this change over o ther variables that were not measured, therefore, it is identified that the influence of the predictors is significant at the 0.000 level. Finally, the heritage module predicts the behavior of the logistics process, since it explains 34.9% of this change over other variables that were not measured, therefore, it is identified that the influence of the predictors is significant at the 0.000 level.