Machine learning applied to forecast intermittent demandin a plastic packaging company

Authors

DOI:

https://doi.org/10.26439/ing.ind2024.n.6715

Keywords:

plastic containers, supply chain, machine learning, sales forecasting, supply and demand, exponential smoothing, forecasting

Abstract

This article compares three demand forecasting methods applied to a Peruvian cosmetic plastic packaging company with intermittent demand. The comparison between the error metrics for forecasting Mean Absolute Error, Mean Percentage Error, and Mean Squared Error obtained by the Croston, Croston TSB, and Exponential Smoothing methods showed that the Croston TSB model outperformed the other two, with an error of less than 20 % compared to actual sales.

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Author Biographies

  • Alex Víctor Sánchez García, Universidad de Lima, Facultad de Ingeniería, Lima, Perú

    Bachiller en Ingeniería Industrial por la Universidad de Lima. Coordinador de operaciones en una empresa líder del sector plásticos. Su interés académico es la analítica de datos y los métodos cuantitativos aplicados a procesos de gestión.

  • José Antonio Taquía Gutiérrez, Universidad de Lima, Facultad de Ingeniería, Lima, Perú

    Doctor en Gestión de Empresas por la Facultad de Ingeniería Industrial de la Universidad Nacional Mayor de San Marcos. Magister en Ingeniería Industrial por la Universidad de Lima. Miembro del Institute of Industrial and Systems Engineers (IISE) de Estados Unidos. Tiene amplia experiencia en el diseño e implementación de tecnología orientada al análisis de datos y en metodología de investigación científica con proyectos desarrollados en operaciones, cadenas de abastecimiento, analítica en retail y servicios de educación.

References

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Published

2024-05-28

Issue

2024: Edición especial

Section

Artículos

How to Cite

Machine learning applied to forecast intermittent demandin a plastic packaging company. (2024). Ingeniería Industrial, 97-109. https://doi.org/10.26439/ing.ind2024.n.6715

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