Arquitectura Predictiva en la Logística de Inventarios: Del Modelo Clásico ABC-XYZ al Análisis de Error Residual
DOI:
https://doi.org/10.70625/rmis/751Keywords:
Cadena de suministro, Segmentación multivariante, Series temporales, Optimización de stock, Incertidumbre genuinaAbstract
Introducción: La gestión contemporánea de cadenas de suministro depende de clasificaciones que frecuentemente confunden variabilidad predecible con incertidumbre genuina, generando niveles de seguridad excesivos e inmovilizando valiosos recursos financieros. La evolución hacia modelos basados en la descomposición de series temporales propone que solo el componente irregular justifica la protección estocástica. Objetivo: Identificar, evaluar y sintetizar críticamente la evidencia científica reciente sobre las metodologías avanzadas que superan el estándar histórico bidimensional para optimizar el capital de trabajo. Metodología: Se ejecutó una revisión de alcance cualitativa y descriptiva fundamentada en los lineamientos estandarizados internacionales. Mediante la búsqueda sistemática en cinco bases de datos de acceso abierto, se consolidó una muestra documental de dieciséis artículos de alta relevancia publicados entre los años dos mil diecinueve y dos mil veinticuatro. Conclusiones: La transición paradigmática hacia algoritmos de pronóstico y segmentación multivariante reduce la varianza efectiva, liberando capital atrapado al aislar patrones estacionales. Superar la métrica tradicional mediante el cálculo basado estrictamente en las desviaciones no anticipadas se ha convertido en una necesidad operativa comprobada. Se requiere actualizar los límites de categorización para transformar estructuralmente la resiliencia en los ecosistemas empresariales de almacenamiento y un reabastecimiento logístico verdaderamente eficiente.
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