DOAJ Open Access 2025

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 +2 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Transportation and communication Management. Industrial management Transportation and communications

Penulis (7)

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 López

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APA MLA BibTeX

Torres, J.A.O., Santiago, A.M., García-Martínez, J.R., López-Zapata, B.Y., López, J.A.M., Zapata, O.J.R. et al. (2025). A Data-Driven Approach Using Recurrent Neural Networks for Material Demand Forecasting in Manufacturing. https://doi.org/10.3390/logistics9030130

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.3390/logistics9030130
Akses: Open Access ✓