SupplyGraph: Supply Chain Planning using GNNs

# SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks
- Authors: [**Azmine Toushik Wasi**](https://azminewasi.github.io/), [**MD Shafikul Islam**](https://www.linkedin.com/in/md-shafikul-islam-sohan/), and [**Adipto Raihan Akib**](https://www.linkedin.com/in/adipto-raihan-akib-739729117/)
- Affiliation: [**Computational Intelligence and Operations Lab - CIOL**](https://ciol-sust.github.io/), SUST
- [**Website**](https://ciol-research.github.io/works/SupplyGraph/) | [arXiv](https://arxiv.org/abs/2401.15299) | [GitHub](https://github.com/ciol-researchlab/SupplyGraph)

---

**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.

---

## Citation:
```
@misc{wasi2024supplygraphbenchmarkdatasetsupply,
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},
url={https://arxiv.org/abs/2401.15299},
}
```
---

***Accepted in [4th workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR Workshop)](https://sites.google.com/view/gclr2024/), [AAAI'24 (38th Annual AAAI Conference on Artificial Intelligence)](https://aaai.org/aaai-conference/).***

供应链图：基于图神经网络进行供应链规划的基准数据集

作者：[**Azmine Toushik Wasi**](https://azminewasi.github.io/), [**MD Shafikul Islam**](https://www.linkedin.com/in/md-shafikul-islam-sohan/), 以及 [**Adipto Raihan Akib**](https://www.linkedin.com/in/adipto-raihan-akib-739729117/)

所属机构：[**计算智能与运营实验室 - CIOL**](https://ciol-sust.github.io/), SUST

[**网站**](https://ciol-research.github.io/works/SupplyGraph/) | [arXiv](https://arxiv.org/abs/2401.15299) | [GitHub](https://github.com/ciol-researchlab/SupplyGraph)

摘要：图神经网络（GNNs）在诸如交通、生物信息学、语言处理和计算机视觉等多个领域得到了广泛关注。然而，将 GNNs 应用于供应链网络的研究却相对匮乏。供应链网络在本质上具有图状结构，使其成为 GNN 方法应用的理想选择。这为优化、预测和解决最复杂的供应链问题开辟了广阔的可能性。这种方法的主要障碍在于缺乏真实世界的基准数据集，以促进使用 GNNs 研究和解决供应链问题。为了解决这一问题，我们提出一个真实世界的基准数据集，用于时间序列任务，该数据集来源于孟加拉国领先的快速消费品公司之一，专注于生产目的的供应链规划。该数据集包含时间序列数据作为节点特征，以实现销售预测、生产规划和工厂问题的识别。通过利用此数据集，研究人员可以使用 GNNs 解决众多供应链问题，从而推动供应链分析和规划领域的发展。

引用：

@misc{wasi2024supplygraphbenchmarkdatasetsupply,
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},
url={https://arxiv.org/abs/2401.15299},
}

***已接受于 [第4届图与更复杂结构用于学习和推理研讨会 (GCLR Workshop)](https://sites.google.com/view/gclr2024/)，[AAAI'24 (第38届人工智能年度 AAAI 会议)](https://aaai.org/aaai-conference/).***

搜集汇总

背景与挑战

背景概述

SupplyGraph是一个专注于供应链规划的基准数据集，特别设计用于图神经网络（GNNs）的研究。数据集包含来自孟加拉国一家领先快速消费品公司的实时数据，支持销售预测、生产规划和工厂问题识别等任务。