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

Jorge Antonio Orozco Torres (), Alejandro Medina Santiago (), José R. García-Martínez, Betty Yolanda López-Zapata, Jorge Antonio Mijangos López, Oscar Javier Rincón Zapata and Jesús Alejandro Avitia López
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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

Logistics, 2025, vol. 9, issue 3, 1-17

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.

Keywords: analytics; forecasting; demand; neural networks; manufacturing; data-driven (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:9:y:2025:i:3:p:130-:d:1748572

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