Spatial Graph Neural Networks for Accurate Product Demand Prediction

Abstract

Accurate product demand prediction is critical for enhancing operational efficiency and strategic planning in supply chain management. Traditional forecasting methods, such as time series analysis and regression models, often fall short in capturing the complex spatial dependencies that influence consumer behavior across different geographical regions. This paper explores the application of Spatial Graph Neural Networks (SGNNs) as an innovative approach to address these limitations. SGNNs extend conventional Graph Neural Networks by integrating spatial information into the graph structure, allowing for a more nuanced understanding of how local market conditions, demographic factors, and competitive dynamics impact product demand. Our study demonstrates that SGNNs significantly outperform traditional forecasting techniques by effectively modeling spatial relationships. Comparative analyses reveal the advantages and limitations of SGNNs relative to other machine learning approaches, emphasizing the importance of spatial considerations in demand prediction. Future research directions include refining SGNN methodologies, incorporating diverse data sources, and exploring applications in various domains beyond supply chain management. This work underscores the potential of SGNNs to transform demand forecasting practices across industries, paving the way for more accurate and reliable prediction techniques.