An explainable machine learning model to optimize demand forecasting in Company DEOS

• Autores: Cabrera Feijoo, Gianella Valeria, Germana Valverde, Jimena Mariana
• Formato: tesis de grado
• Fecha de Publicación: 2023
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/18455
• Enlace del recurso: https://hdl.handle.net/20.500.12724/18455
• Nivel de acceso: acceso abierto
• Materia: Aprendizaje automático; Pronósticos económicos; Machine learning; Economic forecasting; https://purl.org/pe-repo/ocde/ford#2.11.04

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).