A data-driven approach using recurrent neural networks for material demand forecasting in manufacturing

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.