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

arXiv: https://arxiv.org/abs/2401.15299 · HTML: https://mlsanity.com/paper/2401.15299/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Line Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2010.10046/) (2020)
- [Data Considerations in Graph Representation Learning for Supply Chain Networks](https://mlsanity.com/paper/2107.10609/) (2021)
- [Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers](https://mlsanity.com/paper/2006.05608/) (2020)

## Built on by (most cited first, 9 of 9)

- [C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction](https://mlsanity.com/paper/2508.08071/) (2025)
- [Learning production functions for supply chains with graph neural networks](https://mlsanity.com/paper/2407.18772/) (2024)
- [Applying graph neural network to SupplyGraph for supply chain network](https://mlsanity.com/paper/2408.14501/) (2024)
- [Optimizing Supply Chain Networks with the Power of Graph Neural Networks](https://mlsanity.com/paper/2501.06221/) (2025)
- [SynDelay: A Synthetic Dataset for Delivery Delay Prediction](https://mlsanity.com/paper/2509.05325/) (2025)
- [From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience](https://mlsanity.com/paper/2604.11041/) (2026)
- [ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks](https://mlsanity.com/paper/2605.12768/) (2026)
- [ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience](https://mlsanity.com/paper/2606.10359/) (2026)
- [On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions](https://mlsanity.com/paper/2607.16769/) (2026)

## Abstract (arXiv)

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