Leveraging variational autoencoders and recurrent neural networks for demand forecasting in supply chain management: A case study

• Published in: Journal of Infrastructure, Policy and Development Vol. 8; no. 8; p. 6639
• Main Authors: Khlie, Khaoula, Benmamoun, Zoubida, Fethallah, Widad, Jebbor, Ikhlef
• Format: Journal Article
• Language: English
• Published: 26.08.2024
• ISSN: 2572-7923; 2572-7931
• DOI: 10.24294/jipd.v8i8.6639

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.