Hybrid Algorithmic Framework for Intelligent Requisition Management: Integrating Excel, AI Agents, and Power BI for Analytical Traceability
DOI:
https://doi.org/10.61467/2007.1558.2027.v18i1.1464Keywords:
Requisition management, Artificial Intelligence, Procurement; n8n, Gestión de requisiciones, agentes inteligentesAbstract
This research devoelops and validates an algorithmic framework for intelligence requisition management through the integration of Excel, artificial intelligence agents and Power BI. The study adopts a mixed-methods approach with a quasi-experimental design implemented in a metalworking company, aiming to optimize the traceability, classification and monitoring of operational requisitions. The system incorporates a traffic-light algorithm base don criticality indicators, automated alerts and the generation of executive reports using GPT-4º. Additionally, it employs Python, JSON structures and n8n to ensure interoperability and workfow automation. The results showed significant improvements in operational efficiency, highlighting a 63.2% reduction in cycle time, an 82.9% decrease in operational errors and a 94.7% increase in classification accuracy. The framework proved to be scalable, reproducible and efficient for contemporary complex organizational environments.
Spanish-language metadata / Metadatos en español
Título en español:
Marco algorítmico híbrido para la gestión inteligente de requisiciones: integración de Excel, agentes de IA y Power BI para la trazabilidad analítica
Resumen:
Esta investigación desarrolla y valida un marco algorítmico para la gestión inteligente de requisiciones mediante la integración de Excel, agentes de inteligencia artificial y Power BI. El estudio adopta un enfoque de métodos mixtos con un diseño cuasiexperimental implementado en una empresa metalmecánica, con el objetivo de optimizar la trazabilidad, clasificación y seguimiento de las requisiciones operativas.
El sistema incorpora un algoritmo de semaforización basado en indicadores de criticidad, alertas automatizadas y la generación de informes ejecutivos mediante GPT-4o. Además, emplea Python, estructuras JSON y n8n para garantizar la interoperabilidad y la automatización de los flujos de trabajo.
Los resultados mostraron mejoras significativas en la eficiencia operativa, entre las que destacan una reducción del 63,2 % en el tiempo de ciclo, una disminución del 82,9 % en los errores operativos y un incremento del 94,7 % en la precisión de la clasificación. El marco demostró ser escalable, reproducible y eficiente para entornos organizacionales complejos contemporáneos.
Palabras Claves:
Gestión de requisiciones; Inteligencia Artificial; Power BI; agentes inteligentes; automatización; trazabilidad analítica; adquisiciones; n8n.
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