SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912188520726528 |
|---|---|
| author | Wasi, Azmine Toushik Islam, MD Shafikul Akib, Adipto Raihan |
| author_facet | Wasi, Azmine Toushik Islam, MD Shafikul Akib, Adipto Raihan |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks Wasi, Azmine Toushik Islam, MD Shafikul Akib, Adipto Raihan Machine Learning Artificial Intelligence Information Retrieval Systems and Control Applications I.2.1; I.2.8; E.0; J.2; H.3.7 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 |
| title | SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks |
| topic | Machine Learning Artificial Intelligence Information Retrieval Systems and Control Applications I.2.1; I.2.8; E.0; J.2; H.3.7 |
| url | https://arxiv.org/abs/2401.15299 |