SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph
  Neural Networks
Azmine Toushik Wasi MD Shafikul Islam Adipto Raihan Akib
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