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A Data-Driven Approach Using Recurrent Neural Networks for Material Demand Forecasting in Manufacturing

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  • Jorge Antonio Orozco Torres

    (Departamento de Ingeniería Industrial, Tecnológico Nacional de México, Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana Km. 1080, Tuxtla Gutiérrez 29050, Mexico)

  • Alejandro Medina Santiago

    (SECIHTI-National Institute for Astrophysics, Optics and Electronics, Computer Science Coordination, Puebla 72840, Mexico)

  • José R. García-Martínez

    (Laboratorio de Control y Robótica, Facultad de Ingeniería en Electrónica y Comunicaciones, Universidad Veracruzana, Poza Rica 93390, Mexico)

  • Betty Yolanda López-Zapata

    (Dirección de Ingeniería Biomédica, Universidad Politecnica de Chiapas, Carretera Tuxtla Gutierrez-Portillo Zaragoza Km 21+500, Las Brisas, Suchiapa 29150, Mexico)

  • Jorge Antonio Mijangos López

    (Departamento de Ingeniería Industrial, Tecnológico Nacional de México, Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana Km. 1080, Tuxtla Gutiérrez 29050, Mexico)

  • Oscar Javier Rincón Zapata

    (Departamento de Ingeniería Industrial, Tecnológico Nacional de México, Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana Km. 1080, Tuxtla Gutiérrez 29050, Mexico)

  • Jesús Alejandro Avitia López

    (Departamento de Ingeniería Industrial, Tecnológico Nacional de México, Instituto Tecnológico de Tuxtla Gutiérrez, Carretera Panamericana Km. 1080, Tuxtla Gutiérrez 29050, Mexico)

Abstract

Background : In the current context of increasing competitiveness and complexity in markets, accurate demand forecasting has become a key element for efficient production planning. Methods : This study implements recurrent neural networks (RNNs) to predict raw material demand using historical sales data, leveraging their ability to identify complex temporal patterns by analyzing 156 historical records. Results : The findings reveal that the RNN-based model significantly outperforms traditional methods in predictive accuracy when sufficient data is available. Conclusions : Although integration with MRP systems is not explored, the results demonstrate the potential of this deep learning approach to improve decision-making in production management, offering an innovative solution for increasingly dynamic and demanding industrial environments.

Suggested Citation

  • Jorge Antonio Orozco Torres & Alejandro Medina Santiago & José R. García-Martínez & Betty Yolanda López-Zapata & Jorge Antonio Mijangos López & Oscar Javier Rincón Zapata & Jesús Alejandro Avitia Lópe, 2025. "A Data-Driven Approach Using Recurrent Neural Networks for Material Demand Forecasting in Manufacturing," Logistics, MDPI, vol. 9(3), pages 1-17, September.
  • Handle: RePEc:gam:jlogis:v:9:y:2025:i:3:p:130-:d:1748572
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