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Optimizing Supply Chain Networks with the Power of Graph Neural Networks

Chi-Sheng Chen, Ying-Jung Chen

TL;DR

The paper tackles demand forecasting in complex supply chain networks by introducing the SupplyGraph dataset and evaluating Graph Neural Networks (GNNs) against traditional baselines. It compares an MLP baseline, a general GNN, and Graph Convolutional Networks (GCNs) on single-node forecasting tasks, demonstrating that GNNs substantially improve predictive accuracy, especially when modeling time-series data with temporal features. Key contributions include defining downstream supply chain tasks, releasing open-source code, and showing that GNNs—with either learned or self-loop adjacency—effectively learn node-level temporal patterns even with limited explicit inter-node connections. The findings suggest strong practical implications for inventory management, production planning, and logistics optimization, and point to future work on dynamic graphs and learned adjacency for further gains.

Abstract

Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benchmark for graph-based supply chain analysis. By leveraging advanced GNN methodologies, we enhance the accuracy of forecasting models, uncover latent dependencies, and address temporal complexities inherent in supply chain operations. Comparative analyses demonstrate that GNN-based models significantly outperform traditional approaches, including Multilayer Perceptrons (MLPs) and Graph Convolutional Networks (GCNs), particularly in single-node demand forecasting tasks. The integration of graph representation learning with temporal data highlights GNNs' potential to revolutionize predictive capabilities for inventory management, production scheduling, and logistics optimization. This work underscores the pivotal role of forecasting in supply chain management and provides a robust framework for advancing research and applications in this domain.

Optimizing Supply Chain Networks with the Power of Graph Neural Networks

TL;DR

The paper tackles demand forecasting in complex supply chain networks by introducing the SupplyGraph dataset and evaluating Graph Neural Networks (GNNs) against traditional baselines. It compares an MLP baseline, a general GNN, and Graph Convolutional Networks (GCNs) on single-node forecasting tasks, demonstrating that GNNs substantially improve predictive accuracy, especially when modeling time-series data with temporal features. Key contributions include defining downstream supply chain tasks, releasing open-source code, and showing that GNNs—with either learned or self-loop adjacency—effectively learn node-level temporal patterns even with limited explicit inter-node connections. The findings suggest strong practical implications for inventory management, production planning, and logistics optimization, and point to future work on dynamic graphs and learned adjacency for further gains.

Abstract

Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benchmark for graph-based supply chain analysis. By leveraging advanced GNN methodologies, we enhance the accuracy of forecasting models, uncover latent dependencies, and address temporal complexities inherent in supply chain operations. Comparative analyses demonstrate that GNN-based models significantly outperform traditional approaches, including Multilayer Perceptrons (MLPs) and Graph Convolutional Networks (GCNs), particularly in single-node demand forecasting tasks. The integration of graph representation learning with temporal data highlights GNNs' potential to revolutionize predictive capabilities for inventory management, production scheduling, and logistics optimization. This work underscores the pivotal role of forecasting in supply chain management and provides a robust framework for advancing research and applications in this domain.
Paper Structure (18 sections, 6 equations, 1 figure, 2 tables)

This paper contains 18 sections, 6 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Tasks Definition
  4. The Structure of the SupplyGraph Dataset
  5. Demand Forecasting Based on Single Node
  6. Methodology
  7. Multilayer Perceptron (MLP)
  8. Graph Neural Networks (GNN)
  9. Graph Convolutional Networks (GCNs)
  10. Experiments
  11. Data Preprocessing
  12. Performance Metrics
  13. Mean Absolute Error (MAE)
  14. Mean Squared Error (MSE)
  15. Implementation Details
  16. ...and 3 more sections

Figures (1)

  • Figure 1: A conceptual workflow for GNNs demand forecasting for single node.