Author(s)

Bob Kim¹, Ali Ahmed^2*

Affiliation(s)

¹School of Computer Science, University of Technology Sydney, Australia.

²School of Computer Science, University of Sydney, Sydney, Australia.

Corresponding Author

Ali Ahmed

ABSTRACT

This paper examines the integration of spatial Graph Neural Networks (GNNs) into supply chain logistics to enhance demand forecasting accuracy and overall operational efficiency. Supply chain logistics involves the planning, execution, and control of goods and information flow, with effective management directly influencing customer satisfaction and cost efficiency. Demand forecasting is critical for anticipating customer needs and optimizing inventory levels, thereby reducing stockouts and excess inventory. Traditional forecasting methods, while effective, often struggle to capture complex demand patterns influenced by external factors. GNNs, designed to process graph-structured data, offer a novel approach to modeling the intricate relationships within supply chain data. By leveraging the spatial dependencies inherent in logistics networks, GNNs can significantly improve the accuracy of demand predictions. This research evaluates the effectiveness of GNN-based forecasting methods through a review of existing literature and case studies, providing insights for practitioners aiming to enhance their supply chain operations. The findings highlight the potential of advanced predictive models in transforming supply chain logistics and emphasize the importance of adopting innovative technologies in an increasingly data-driven environment.

KEYWORDS

Supply chain logistics; Demand forecasting; Graph neural networks

CITE THIS PAPER

Bob Kim, Ali Ahmed. Optimizing supply chain logistics using spatial GNN-based demand predictions. World Journal of Economics and Business Research. 2024, 2(1): 1-9. DOI: 10.61784/wjebr3006.

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