Publication:
Design of Self-regulating Planning Model

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 Design of Self-regulating Planning Model.pdf (1.88 MB)
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Authors

Espitia Rincon, Maria Paula
Sanabria Martínez, David Alejandro
Abril Juzga, Kevin Alberto
Santos Hernández, Andrés Felipe
Abstract (Spanish)
Propósito: Esta investigación tiene como objetivo desarrollar una aplicación dinámica y autorregulada que considere los pronósticos de demanda, basados ​​en la regresión lineal como algoritmo básico para el aprendizaje automático. Metodología: Esta investigación utiliza la planificación agregada y el aprendizaje automático junto con las políticas de inventario a través de la herramienta solver excel para tomar decisiones óptimas en el centro de distribución para reducir costos y garantizar el nivel de servicio. Hallazgos: Los hallazgos de este estudio se refieren a la planificación de tácticas de suministro en tiempo real, la autorregulación de la información en tiempo real y la optimización de la frecuencia del suministro. Originalidad: Una aplicación susceptible de ser actualizada en tiempo real mediante la actualización de datos por parte del director de planificación, que mostrará la planificación agregada óptima y los indicadores de los costos asociados a la operación de picking de una empresa con 12000 SKU's (Stock Keeping Unit), en el que se realiza un comercio minorista de 65 tiendas.
Abstract (English)
Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the solver excel tool to make optimal decisions at the distribution center to reduce costs and guarantee the level of service. Findings: The findings after this study pertain to planning supply tactics in real-time, self-regulation of information in real-time and optimization of the frequency of the supply. Originality: An application capable of being updated in real-time by updating data by the planning director, which will show the optimal aggregate planning and the indicators of the costs associated with the picking operation of a company with 12000 SKU's (Stock Keeping Unit), in which a retail trade of 65 stores is carried out.
Extent
33 páginas.
URI: https://repositorio.escuelaing.edu.co/handle/001/1855
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AI - Centro de Investigación en Manufactura y Servicios – CIMSER
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