- Authors: Azmine Toushik Wasi, MD Shafikul Islam, and Adipto Raihan Akib, Mahathir M. Bappy
📌 TL;DR: This paper introduces a real-world graph dataset empowering researchers to leverage GNNs for supply chain problem-solving, enhancing production planning capabilities, with benchmark scores on six homogeneous graph tasks.
Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks using this dataset. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.
For coding instructions, check code\coding_directions.md
@misc{wasi2024supplygraph,
title={SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks},
author={Azmine Toushik Wasi and MD Shafikul Islam and Adipto Raihan Akib},
year={2024},
eprint={2401.15299},
archivePrefix={arXiv},
primaryClass={cs.LG}
}