Bibliographic citations
Mendoza, R., Prudencio, E. (2023). Módulo de órdenes de compra de importación usando una biblioteca de machine learning para un gestor de ventas de una empresa retail [Trabajo de Suficiencia Profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/670278
Mendoza, R., Prudencio, E. Módulo de órdenes de compra de importación usando una biblioteca de machine learning para un gestor de ventas de una empresa retail [Trabajo de Suficiencia Profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2023. http://hdl.handle.net/10757/670278
@misc{renati/409920,
title = "Módulo de órdenes de compra de importación usando una biblioteca de machine learning para un gestor de ventas de una empresa retail",
author = "Prudencio Leon, Edson Eduardo",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2023"
}
Reducing excess or shortages of products in a retail company can be a very challenging process, due to the bad practices used when importing in this type of company. The main consequence of this is that the inventory does not have a sufficient supply of products due to the lack of a correct prediction of the stock. A purchase order is not usually a complex concept to understand or execute within a routine purchasing strategy. However, in the case of manual purchase orders the issue is complicated because they often result in inefficient processes. This usually happens because a purchase order is mainly linked to decision making. In this work, a purchase order module was implemented within the company's sales system. To do this, the TensorFlow library was used, which was responsible for predicting the demand quantities for a certain period. Likewise, it was decided to develop this module in the Python programming language. The result obtained was to reduce the time in generating purchase orders since the warehouseman obtained the information in an agile manner. There was also a notable improvement in meeting customer demand without having so much excess merchandise in warehouse.
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