An Explainable Machine Learning Model to Optimize Demand Forecasting in Company DEOS

Gianella Cabrera Feijoo, Jimena Germana Valverde, Yvan Jesus Garcia Lopez

Research output: Chapter in Book/Report/Conference proceedingPaper (Conference contribution)peer-review


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).
Translated title of the contributionUn modelo explicable de machine learning para optimizar la previsión de la demanda en CompanyDEOS
Original languageEnglish
Title of host publicationISSN / E-ISSN: 2169-8767: Proceedings of the International Conference on Industrial Engineering and Operations Management
Subtitle of host publicationIEOM Society
Place of PublicationINDIA
PublisherIEOM Society International
Pages1 - 12
Number of pages12
ISBN (Electronic)978-1-7923-9160-6
ISBN (Print)2169-8767 (U.S. Library of Congress)
StatePublished - 16 Aug 2022


  • Article

OECD Category

  • Ingeniería industrial

Ulima Repository Category

  • Ingeniería industrial / Teoría

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