Artificial intelligence demand forecasting for improved inventory and fleet management
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Abstract
This study analyzes the impact of Artificial Intelligence (AI) on demand forecasting as a strategy to optimize inventory and logistics fleet management. Advanced models such as recurrent neural networks (RNN), Transformers, and Gradient Boosting were compared with traditional statistical methods such as ARIMA and Exponential Smoothing. The results showed that AI-based models reduced forecast error by up to 50%, decreased logistics costs by 31.5%, and reduced empty kilometers by 15%. In addition, the potential of AI to reduce CO? emissions was demonstrated, aligning with sustainability goals. However, significant challenges were identified, such as the need for high-quality data and specialized technical training. Future research lines focused on the scalability of AI for SMEs and its integration with blockchain are proposed.
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