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El registro manual de existencias y las prácticas heurísticas de gestión de pedidos siguen siendo habituales entre las microempresas y las pequeñas y medianas empresas (MIPYMES), lo que a menudo da lugar a estimaciones inexactas de la demanda, costes de almacenamiento excesivos y roturas de stock. Este estudio desarrolla y evalúa un sistema de información de inventario basado en la web que integra la previsión mediante el modelo ARIMA (Autoregressive Integrated Moving Average) con el modelo de cantidad económica de pedido (EOQ) para mejorar la precisión de las decisiones y la eficiencia en los costes. El sistema utiliza CodeIgniter 3 y MySQL e incorpora un motor de previsión de series temporales basado en Python. Los datos históricos de ventas se modelizaron utilizando ARIMA, y la especificación óptima se seleccionó basándose en el Criterio de Información de Akaike (AIC) y el Criterio de Información Bayesiano (BIC). El modelo ARIMA(1,1,1) alcanzó un error porcentual absoluto medio (MAPE) del 8,47 %, lo que indica una alta precisión en la previsión para la planificación operativa. La demanda anual prevista se integró en el marco de la cantidad óptima de pedido (EOQ) para determinar la cantidad óptima de pedido, el punto de reabastecimiento (ROP) y el stock de seguridad probabilístico. Una simulación de costes a un año demostró que la política basada en la EOQ reducía los costes totales de inventario en un 22,73 % en comparación con el enfoque existente. La validación funcional mediante pruebas de «caja negra» confirmó el pleno cumplimiento de los requisitos especificados. Estos resultados demuestran que la integración del análisis predictivo con la optimización clásica de inventarios mejora la eficiencia operativa y reduce el coste total de inventario. El sistema proporciona un marco práctico de gestión de inventarios basado en datos para las micro, pequeñas y medianas empresas (MIPYMES) que están llevando a cabo una transformación digital.
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2026 Alkindi Syamsi, Dedy Irfan, Dony Novaliendry, Randi Proska Sandra