Abstract

Demand forecasting is a critical function in supply chain management, enabling businesses to predict customer needs and optimize inventory, production, and logistics processes. Traditional forecasting methods, such as ARIMA and exponential smoothing, have been widely used due to their simplicity and interpretability. However, the growing complexity of market dynamics and data patterns has revealed limitations in their accuracy and adaptability. Recent advancements in Artificial Intelligence (AI) have introduced machine learning and deep learning-based models, such as Random Forest and Long Short-Term Memory (LSTM) networks, which offer enhanced performance in handling non-linear and high-dimensional data. This study presents a comparative analysis of traditional and AI-based forecasting models, focusing on their accuracy, computational efficiency, scalability, and interpretability. Using diverse datasets from industries such as retail, manufacturing, and e-commerce, the research evaluates the strengths and weaknesses of each approach. The findings highlight the conditions under which AI-based models significantly outperform traditional methods and discuss the trade-offs involved in resource consumption and ease of deployment. Practical recommendations and future trends, including hybrid models and explainable AI, are proposed to guide businesses in selecting the most appropriate forecasting techniques.