El Impacto de la Aplicación del Análisis de Datos con Python y Power BI en la Optimización de Procesos Contables en Logística Internacional

Abstract

The present study aimed to analyze the impact of implementing Python and Microsoft Power BI, together with data analysis strategies supported by interactive dashboards and KPI-driven reports, on the efficiency and accuracy of bank reconciliation and cash flow planning processes within the accounting department of a United States–based international logistics company. Employing a mixed-methods design, the research combined quantitative performance measurements collected before and after project implementation with a qualitative semi-structured interview conducted with the general manager to gain contextual insights. The results revealed a 63 % reduction in time required for the processes examined, enhanced precision in identifying transactions for auditing purposes, and improved detection of customer payment behavior patterns. These improvements contributed to streamlined workflows, reduced manual effort, and more informed strategic cash flow planning decisions. The study concludes that integrating Python and Power BI for data analysis, alongside customized dashboards and financial key performance indicators (KPIs), significantly optimized the accounting department’s operational metrics and decision-making capabilities. The findings provide empirical evidence supporting the adoption of advanced analytics tools in accounting operations within the logistics sector.