Comparative Analysis of Deep Learning vs. Traditional Forecasting Models for Inventory Control

Effective inventory control is essential for optimizing operational efficiency and reducing costs in supply chains. Accurate demand forecasting lies at the heart of this process. Traditionally, models such as ARIMA, Exponential Smoothing, and Moving Average have been widely used for inventory forecasting. However, recent advancements in Artificial Intelligence (AI), particularly Deep Learning (DL), have enabled more accurate, adaptive, and nonlinear demand predictions. This research presents a comparative analysis of Deep Learning models (LSTM and CNN) and Traditional Forecasting models (ARIMA and Exponential Smoothing) for inventory control. Using real-world sales datasets, we assess prediction accuracy, adaptability to demand fluctuations, and computational efficiency. The study concludes that Deep Learning models outperform traditional approaches in complex, dynamic demand environments, offering significant improvements in forecast accuracy and inventory optimization.