Predicting Supply Shocks Before They Propagate Using Temporal Graph Reasoning
DOI:
https://doi.org/10.54097/k79vxk50Keywords:
Supply chain disruption, temporal graph neural network, shock propagation, dynamic graph reasoning, supply chain resilience, graph convolutional network, early warning systemsAbstract
Supply shocks in modern production networks rarely originate from isolated events; they propagate upstream and downstream through densely interconnected supplier-customer relationships, often manifesting far from their point of origin before conventional monitoring systems detect any signal. This paper proposes a temporal graph reasoning framework for predicting supply shocks before they complete their propagation cascade. By modeling supplier-customer networks as dynamic directed graphs with time-stamped transaction edges, the proposed architecture integrates diffusion-based spatial aggregation with recurrent temporal encoding to capture the evolving topology of inter-firm dependencies. A graph convolutional network (GCN) backbone encodes node-level structural features, while a long short-term memory (LSTM) layer learns the sequential dynamics of nodal activity. The model is evaluated against static graph baselines and non-graph sequential models on a multi-industry transaction dataset spanning 36 months. Results demonstrate that the proposed approach achieves a mean absolute error (MAE) reduction of 18.3% over the best competing baseline and exhibits substantially earlier detection capability, identifying disruption precursors an average of 11.3 days before shock propagation reaches tier-1 suppliers. These findings suggest that temporal graph reasoning constitutes a promising direction for proactive supply chain risk management under conditions of structural uncertainty.
Downloads
References
[1] Queiroz, M. M., Fosso Wamba, S., & Branski, R. M. (2022). Supply chain resilience during the COVID-19: empirical evidence from an emerging economy. Benchmarking: An International Journal, 29(6), 1999-2018.
[2] Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of big data, 7(1), 53.
[3] Wei, Z., Sun, T., & Zhou, M. (2024). LIRL: Latent Imagination-Based Reinforcement Learning for Efficient Coverage Path Planning. Symmetry, 16(11), 1537.
[4] Zhang, S., Qiu, L., & Zeng, Z. (2026). Physics-Data Synergy in Structural Health Monitoring: A Multi-Scale Graph Contrastive Framework With Temperature-Adaptive Fusion. IEEE Access.
[5] Zeng, Z., Lin, H., Zhang, S., & Wang, B. (2026). Adaptive Robust Watermarking for Large Language Models via Dynamic Token Embedding Perturbation. IEEE Access, 14, 9319-9339.
[6] Qiu, L. (2025). Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across Urban Communities. Computer Life, 13(3), 8-15.
[7] Zhao, W., Chen, T., Yang, J. S., & Qiu, L. (2026). AutoML-Pipeline: A RAG-enhanced code generation framework with pre-validation for cloud-native machine learning workflows. IEEE Access.
[8] Yang, Y., & Yang, J. (2026). Synthetic Data Meets Finance: Generative Models for Privacy Preserving Analytics. Journal of Banking and Financial Dynamics, 10(4), 1-8.
[9] Wang, Z., Shen, Z., Wang, B., & Shang, W. (2025). Modernizing Enterprise Analytics through Low-Code Automation and Cloud-Native Data Architectures. Asian Business Research Journal, 10(12), 20-33.
[10] Zhao, X., Sun, T., Ren, S., Yang, J., & Liu, Y. (2025). RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment. Frontiers in Artificial Intelligence Research, 2(3), 501-520.
[11] Li, P., Liu, J., & Qiu, L. (2026). Deep Learning Methods for Demand Forecasting and Inventory Optimization in Modern Supply Chains. Asian Business Research Journal, 11(3), 21-29.
[12] Qiu, L. (2025). Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions. Journal of Computing and Electronic Information Management, 18(2), 32-37.
[13] Zhang, H. (2025). Reinforcement Learning Approaches for Layout Optimization in Electronic Design Automation with Electromagnetic Compatibility Constraints. Frontiers in Robotics and Automation, 2(2), 77-93.
[14] Shen, Z., Zhao, W., Wang, B., Wang, Z., & Shang, W. (2026). CAGR: A Cross-Accelerator Graph Optimization Framework for Efficient Recommender System Inference. IEEE Access.
[15] Sun, T., Wang, M., & Han, X. (2025). Deep Learning in Insurance Fraud Detection: Techniques, Datasets, and Emerging Trends. Journal of Banking and Financial Dynamics, 9(8), 1-11.
[16] Liu, J., Li, P., & Wang, Y. (2026). Graph Neural Networks for Modeling Complex Dependencies in Global Supply Chain Networks. Journal of Computing and Electronic Information Management, 20(3), 9-20.
[17] Zhang, F., & Wu, B. (2025). Large Language Models as General Purpose Intelligence Systems for Reasoning, Planning and Decision Making. American Journal of Artificial Intelligence and Neural Networks, 6(4), 45-72.
[18] Li, P., Ren, S., Zhang, Q., Wang, X., & Liu, Y. (2024). Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access, 12, 195974-195985.
[19] Zhang, F., & Yang, J. S. (2025). Learning Driven Decision Intelligence for Autonomous Driving Through Multimodal Understanding World Modeling and Policy Optimization. Frontiers in Artificial Intelligence Research, 2(3), 616-634.
[20] Wang, B., Wang, Z., Zhao, W., & Liu, Y. (2025). Network Fabric Simulation and Validation for Data Center Routing Convergence Under Large-Scale Failure Scenarios. Computer Science Bulletin, 8(01), 310-326.
[21] Liu, J., Wang, J., Chen, H., Guinness, J., Martin, R., & Kulkarni, C. S. (2019). Optimal Level Crossing Predictions for Electronic Prognostics. In AIAA Scitech 2019 Forum (p. 1962).
[22] Chen, J., Cui, Y., Zhang, X., Yang, J., & Zhou, M. (2024). Temporal convolutional network for carbon tax projection: A data-driven approach. Applied Sciences, 14(20), 9213.
[23] Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proceedings of ICLR 2018. OpenReview.net; 2018.
[24] You, J., Du, T., & Leskovec, J. (2022, August). Roland: graph learning framework for dynamic graphs. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (pp. 2358-2366).
[25] Ding, J., Chen, T., & Qin, Y. (2026). Achieving Resource Isolation in Multi-Tenant Cloud Platforms Without Sacrificing Performance. Journal of Computing and Electronic Information Management, 21(1), 10-18.
[26] Ding, J., & Qin, Y. (2026). Raft and Beyond: Practical Consensus Mechanisms for Geo-Distributed Data Systems. Computer Life, 14(1), 54-63.
[27] Chen, T., & Ding, J. (2026). Cold Start Latency Optimization Strategies for Function as a Service Platforms. Computer Life, 14(1), 64-73.
[28] Wang, X., Zhang, M., Qi, Y., & Wang, Q. (2025). Developing supply chain resilience: a supply network perspective. Supply Chain Management: An International Journal, 30(3), 353-368.
[29] Boehm, C. E., Flaaen, A., & Pandalai-Nayar, N. (2019). Input linkages and the transmission of shocks: Firm-level evidence from the 2011 Tōhoku earthquake. Review of Economics and Statistics, 101(1), 60-75.
[30] Scheibe, K. P., & Blackhurst, J. (2019). Systemic risk and the ripple effect in the supply chain. In Handbook of Ripple Effects in the Supply Chain (pp. 85-100). Cham: Springer International Publishing.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Advanced Engineering and Technology Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
