article · Journal of Infrastructure Policy and Development
Demand forecasting is essential for commercial enterprises seeking to optimise stock levels and meet customer needs efficiently. This research investigates the use of generative artificial intelligence models for forecasting demand, specifically comparing Long Short-Term Memory networks with Variational Autoencoders to identify the better performing approach. By evaluating the variance between actual and predicted demand, the findings confirm that Long Short-Term Memory networks successfully capture latent features and underlying trends in data. The investigation also examines computational efficiency and scalability, aiming to deliver practical guidance for businesses seeking to deploy advanced forecasting methods. Ultimately, the results indicate that Long Short-Term Memory networks offer a viable means to improve demand predictions, supporting more effective decision-making in inventory management and broader resource allocation.
Accurate forecasting prevents both stock shortages and expensive overstocking. Demonstrating how artificial intelligence tools such as recurrent neural networks can reliably predict market demand helps enterprises streamline their supply chains. Understanding the computational demands and scalability of these techniques allows organisations to select practical, effective data-driven methods for planning and resource allocation.
This work is relevant to enterprise supply chain managers and logistics software developers seeking more reliable inventory planning tools. Focused on algorithmic performance, computational efficiency, and scalability guidelines, the research represents an applied and tested stage of development. Organisations can use these comparative insights to implement Long Short-Term Memory models into digital inventory systems to improve stock control and operational resource deployment.
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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.
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DOI: 10.24294/jipd.v8i8.6639
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