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Biblioteca Digital - Universidad Icesi
Master thesis . 2021
License: CC BY NC ND
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Modelo de aprendizaje no supervisado para la priorización de inventarios cíclicos.

Authors: Nakano Edgar, Ryuma Jonathan; Torres, Edgar Felipe;

Modelo de aprendizaje no supervisado para la priorización de inventarios cíclicos.

Abstract

Es común encontrar discrepancias en los sistemas de información de inventarios, entre las cantidades de los productos que el sistema dice tener y las cantidades físicas en la bodega. Existen múltiples causas para esto, principalmente errores humanos y del propio sistema de información. Estas discrepancias pueden ocasionar serios problemas tanto en las operaciones de bodega como en la relación con los clientes, ya que pueden generar demoras en los despachos, y peor aún, compromisos con clientes imposibles de cumplir. Los conteos cíclicos periódicos son una herramienta útil para corregirlas, pero para que sean efectivos, es de vital importancia identificar las discrepancias lo más temprano posible, y así lograr corregirlas antes de que tengan consecuencias negativas para el negocio. Lo anterior no es nada fácil de lograr cuando se tienen bodegas amplias con un alto número de productos.

Country
Colombia
Related Organizations
Keywords

Control de inventarios, Tésis, Almacenamiento de datos, Minería de datos, Database design, Integración de datos (Computadores), Data warehousing, Departamento Tecnologías de Información y Comunicaciones, Inventarios, Inventarios asistidos por computadora, Cadena de abastecimiento, Database management, Ingenierías, Data integration (Computer science), Data mining, Modelos de aprendizaje, Sistemas de información, Inventory control, Administración de bases de datos

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green