SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks

2024-01-27

arXiv.org

AI Summary

Existing research on graph neural networks (GNNs) for supply chain planning is hindered by the absence of realistic, temporal graph benchmark datasets that capture both structural topology and dynamic node attributes. Method: We introduce the first industrial-scale, open-source, and reproducible temporal graph dataset for supply chain planning, derived from a leading Bangladeshi fast-moving consumer goods enterprise. It encompasses over 10 manufacturing plants, 100+ SKUs, and 18 months of daily-resolution data, explicitly modeling supply chain topology alongside time-evolving node features—including sales, inventory, and production capacity. Contribution/Results: We propose the first systematic temporal graph learning framework tailored to multi-task supply chain intelligence, including demand forecasting, production scheduling, and factory anomaly detection. This work bridges the critical gap in real-world GNN benchmarks, establishing a standardized evaluation platform and methodological foundation for intelligent, data-driven supply chain decision-making.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. Source: https://github.com/CIOL-SUST/SupplyGraph

Problem

• Graph Neural Networks
• Supply Chain Planning
• Dataset Limitations

Innovation

• Graph Neural Networks
• Supply Chain Planning
• Time Series Data