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Cabrera, G., Germana, J. (2023). An explainable machine learning model to optimize demand forecasting in Company DEOS [Universidad de Lima]. https://hdl.handle.net/20.500.12724/18455
Cabrera, G., Germana, J. An explainable machine learning model to optimize demand forecasting in Company DEOS []. PE: Universidad de Lima; 2023. https://hdl.handle.net/20.500.12724/18455
@misc{renati/235871,
title = "An explainable machine learning model to optimize demand forecasting in Company DEOS",
author = "Germana Valverde, Jimena Mariana",
publisher = "Universidad de Lima",
year = "2023"
}
Full metadata record
García López, Yván Jesús
Cabrera Feijoo, Gianella Valeria
Germana Valverde, Jimena Mariana
2023-07-03T15:23:28Z
2023-07-03T15:23:28Z
2023
Cabrera Feijoo, G. V. & Germana Valverde, J. M. (2023). An explainable machine learning model to optimize demand forecasting in Company DEOS [Tesis para optar el Título Profesional de Ingeniero Industrial, Universidad de Lima]. Repositorio institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/18455 (es_PE)
https://hdl.handle.net/20.500.12724/18455
Nowadays, having an accurate demand forecast is extremely important as it allows the company to manage resources in an optimal way and thus achieve greater productivity. There is a large demand for accurate forecasting, and utilizing artificial intelligence can help companies gain a better understanding of their market. In this research presentation, Machine Learning (ML) is used to optimize demand forecasting. The data collected was trained and due to the available data rate, the Cross-Validation technique was used to avoid overfitting. Using time-series, it will be possible to predict future sales for the first trimester of 2021. Finally, the impact of the ML tool on the deviation of the company's demand forecast was evaluated using indicators of accuracy (forecast accuracy) and bias (forecast bias). (es_PE)
application/pdf
spa
Universidad de Lima (es_PE)
info:eu-repo/semantics/openAccess (*)
https://creativecommons.org/licenses/by-nc-sa/4.0/ (*)
Repositorio Institucional. Ulima
Universidad de Lima
Aprendizaje automático (es_PE)
Pronósticos económicos (es_PE)
Machine learning (es_PE)
Economic forecasting (es_PE)
Ingeniería industrial / Diseño e innovación tecnológica (es_PE)
An explainable machine learning model to optimize demand forecasting in Company DEOS (es_PE)
info:eu-repo/semantics/bachelorThesis
Universidad de Lima. Facultad de Ingeniería y Arquitectura (es_PE)
Ingeniería Industrial (es_PE)
Título profesional (es_PE)
Ingeniero Industrial (es_PE)
PE (es_PE)
https://purl.org/pe-repo/ocde/ford#2.11.04
http://purl.org/pe-repo/renati/level#tituloProfesional (*)
6074453
https://orcid.org/0000-0001-9577-4188
74124685
70452360
722026
Flores Pérez, Alberto Enrique
Quiroz Flores, Juan Carlos
García López, Yván Jesús
https://purl.org/pe-repo/renati/type#tesis (*)
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