mlsanityData to 14 Oct 2026

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

arXiv 2411.08550 ↗First posted 13 Nov 2024cs.LG cs.CE stat.MLcited by 7

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Abstract (arXiv)

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 discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, 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. 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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 25 of its 59 references that are in the map. First posted on arXiv 13 Nov 2024.

Builds on

25 of 59 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
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13 more
  1. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  2. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 275
  3. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  4. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 165
  5. 2021End-to-End Constrained Optimization Learning: A Surveycited by 157
  6. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  7. 2020Line Graph Neural Networks for Link Predictioncited by 89
  8. 2021Heterogeneous Temporal Graph Neural Networkcited by 23
  9. 2021Data Considerations in Graph Representation Learning for Supply Chain Networkscited by 10
  10. 2021Network In Graph Neural Networkcited by 8
  11. 2024BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filteringcited by 5
  12. 2020Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makerscited by 3
  13. 2024HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025Large Language Models are Good Relational Learnerscited by 24
  2. 2025A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimizationcited by 2
  3. 2026From Mice to Trains: Amortized Bayesian Inference on Graph Datacited by 2
  4. 2025InterCorpRel-LLM: Enhancing Financial Relational Understanding with Graph-Language Modelscited by 1
  5. 2025An Intelligent Multi-task Supply Chain Model Based on Bio-inspired Networkscited by 0
  6. 2025Improving a Hybrid Graphsage Deep Network for Automatic Multi-objective Logistics Management in Supply Chaincited by 0
  7. 2026ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenancecited by 0