Skip to main navigation menu Skip to main content Skip to site footer

Vol. 5 No. 1 (2024)
2024 Edition

Articles

AI-Driven Vulnerability Assessment and Early Warning Mechanism for Semiconductor Supply Chain Resilience

Published 2024-07-08

  • Chengru Ju
    • ,
  • Xiaoxiao Jiang
    • ,
  • Jiang Wu
    • ,
  • Chunhe Ni

Chengru Ju
Public Administration, Columbia University, New York City, NY, USA

Xiaoxiao Jiang
Computer Science & Engineering, Santa Clara University, CA, USA

Jiang Wu
Computer Science, University of Southern California, Los Angeles, CA, USA

Chunhe Ni
Computer Science, University of Texas at Dallas, Richardson, TX, USA

Abstract

This article shows a comprehensive AI-controlled early warning system to detect and predict vulnerabilities in the semiconductor industry supply chain. The study integrates advanced machine learning techniques into online scientific approaches to develop a solid framework for the risk assessment and mitigation of the supply chain. The system architecture includes several data streams, including real-time sensor information, delivery network information, and market indicators that have been treated with a hybrid model by combining graph nerve networks (GNN) with long short-term memory (LSTM) networks. The system achieved 94.3% accuracy in predicting disruption impacts, with an average lead time of 15.3 days for major events. The research methodology included extensive validation across 158 semiconductor manufacturers over 18 months, demonstrating a 64% reduction in disruption impact duration and generating cost savings of $37.2 million. The hybrid pattern architecture outperformed always with the precision value of 0.948 and back at 0.951. This study makes sense of the use of AI for the use of realistic habits and teachings in the strings together. Activity Meeting Services Give Information for Administrative Development Development of Early Property Reminders.

Keywords

Supply Chain Risk Management, Machine Learning, Network Science, Early Warning System

View PDF

References

  1. Raoul Ramirez, Z., & Le, T. V. Semiconductor Supply Chain Resilience: Systematic Review, Conceptual Framework, Implementation Challenges, and Future Research Directions. The V., Semiconductor Supply Chain Resilience: Systematic Review, Conceptual Framework, Implementation Challenges, and Future Research Directions.
  2. Karl, A. A., Micheluzzi, J., Leite, L. R., & Pereira, C. R. (2018). Supply chain resilience and key performance indicators: a systematic literature review. Production, 28, e20180020.
  3. Mann, M., & Putsche, V. (2022). Semiconductor: Supply Chain Deep Dive Assessment. USDOE Office of Policy (OP), Washington DC (United States).
  4. Ju, C. (2023). A Machine Learning Approach to Supply Chain Vulnerability Early Warning System: Evidence from US Semiconductor Industry. Journal of Advanced Computing Systems, 3(11), 21-35.
  5. Singh, C. S., Soni, G., & Badhotiya, G. K. (2019). Performance indicators for supply chain resilience: review and conceptual framework. Journal of Industrial Engineering International, 15(Suppl 1), 105-117.
  6. Xia, S., Zhu, Y., Zheng, S., Lu, T., & Ke, X. (2024). A Deep Learning-based Model for P2P Microloan Default Risk Prediction. International Journal of Innovative Research in Engineering and Management, 11(5), 110-120.
  7. Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.
  8. Liu, Y., Xu, Y., & Zhou, S. (2024). Enhancing User Experience through Machine Learning-Based Personalized Recommendation Systems: Behavior Data-Driven UI Design. Authorea Preprints.
  9. Xu, Y., Liu, Y., Wu, J., & Zhan, X. (2024). Privacy by Design in Machine Learning Data Collection: An Experiment on Enhancing User Experience. Applied and Computational Engineering, 97, 64-68.
  10. Xu, X., Xu, Z., Yu, P., & Wang, J. (2025). Enhancing User Intent for Recommendation Systems via Large Language Models. Preprints.
  11. Li, L., Xiong, K., Wang, G., & Shi, J. (2024). AI-Enhanced Security for Large-Scale Kubernetes Clusters: Advanced Defense and Authentication for National Cloud Infrastructure. Journal of Theory and Practice of Engineering Science, 4(12), 33-47.
  12. Yu, P., Xu, X., & Wang, J. (2024). Applications of Large Language Models in Multimodal Learning. Journal of Computer Technology and Applied Mathematics, 1(4), 108-116.
  13. Shen, Q., Zhang, Y., & Xi, Y. (2024). Deep Learning-Based Investment Risk Assessment Model for Distributed Photovoltaic Projects. Journal of Advanced Computing Systems, 4(3), 31-46.
  14. Chen, J., Zhang, Y., & Wang, S. (2024). Deep Reinforcement Learning-Based Optimization for IC Layout Design Rule Verification. Journal of Advanced Computing Systems, 4(3), 16-30.
  15. Ju, C. (2023). A Machine Learning Approach to Supply Chain Vulnerability Early Warning System: Evidence from US Semiconductor Industry. Journal of Advanced Computing Systems, 3(11), 21-35.
  16. Ju, C., & Ma, X. (2024). Real-time Cross-border Payment Fraud Detection Using Temporal Graph Neural Networks: A Deep Learning Approach. International Journal of Computer and Information Systems (IJCIS), 5(1), 103-114.
  17. Wang, S., Hu, C., & Jia, G. (2024). Deep Learning-Based Saliency Assessment Model for Product Placement in Video Advertisements. Journal of Advanced Computing Systems, 4(5), 27-41.
  18. Pu, Y., Chen, Y., & Fan, J. (2023). P2P Lending Default Risk Prediction Using Attention-Enhanced Graph Neural Networks. Journal of Advanced Computing Systems, 3(11), 8-20.
  19. Jin, M., Zhang, H., & Huang, D. (2024). Deep Learning-Based Early Warning Model for Continuous Glucose Monitoring Data in Diabetes Management. Integrated Journal of Science and Technology, 1(2).
  20. Ma, X., & Jiang, X. (2024). Predicting Cross-border E-commerce Purchase Behavior in Organic Products: A Machine Learning Approach Integrating Cultural Dimensions and Digital Footprints. International Journal of Computer and Information System (IJCIS), 5(1), 91-102.
  21. Xiong, K., Cao, G., Jin, M., & Ye, B. (2024). A Multi-modal Deep Learning Approach for Predicting Type 2 Diabetes Complications: Early Warning System Design and Implementation.
  22. Fan, J., Trinh, T. K., & Zhang, H. (2024). Deep Learning-Based Transfer Pricing Anomaly Detection and Risk Alert System for Pharmaceutical Companies: A Data Security-Oriented Approach. Journal of Advanced Computing Systems, 4(2), 1-14.
  23. Xi, Y., Jia, X., & Zhang, H. (2024). Real-time Multimodal Route Optimization and Anomaly Detection for Cross-border Logistics Using Deep Reinforcement Learning. International Journal of Computer and Information System (IJCIS), 5(2), 102-114.
  24. Chen, J., & Wang, S. (2024). A Deep Reinforcement Learning Approach for Network-on-Chip Layout Verification and Route Optimization. International Journal of Computer and Information System (IJCIS), 5(1), 67-78.
  25. Jia, X., Zhang, H., Hu, C., & Jia, G. (2024). Joint Enhancement of Historical News Video Quality Using Modified Conditional GANs: A Dual-Stream Approach for Video and Audio Restoration. International Journal of Computer and Information System (IJCIS), 5(1), 79-90.
  26. Zhang, H., Jia, X., & Chen, C. (2025). Deep Learning-Based Real-Time Data Quality Assessment and Anomaly Detection for Large-Scale Distributed Data Streams.
  27. Ye, B., Xi, Y., & Zhao, Q. (2024). Optimizing Mathematical Problem-Solving Reasoning Chains and Personalized Explanations Using Large Language Models: A Study in Applied Mathematics Education. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 3(1), 67-83.
  28. Wang, J., Zhao, Q., & Xi, Y. (2025). Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior. Annals of Applied Sciences, 6(1).
  29. Ju, C., Shen, Q., & Ni, X. (2024). Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets. Applied and Computational Engineering, 112, 47-53.
  30. Ju, C., Liu, Y., & Shu, M. (2024). Performance evaluation of supply chain disruption risk prediction models in healthcare: A multi-source data analysis.
  31. Ma, D., Jin, M., Zhou, Z., Wu, J., & Liu, Y. (2024). Deep Learning-Based ADL Assessment and Personalized Care Planning Optimization in Adult Day Health Center. Applied and Computational Engineering, 118, 14-22.
  32. Wei, M., Wang, S., Pu, Y., & Wu, J. (2024). Multi-Agent Reinforcement Learning for High-Frequency Trading Strategy Optimization. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 2(1), 109-124.
  33. Wen, X., Shen, Q., Wang, S., & Zhang, H. (2024). Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems. Applied and Computational Engineering, 96, 107-112.
  34. Yu, P., Xu, Z., Wang, J., & Xu, X. (2025). The Application of Large Language Models in Recommendation Systems. arXiv preprint arXiv:2501.02178.
  35. Yi, J., Xu, Z., Huang, T., & Yu, P. (2025). Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions. arXiv preprint arXiv:2502.00339.
  36. Huang, T., Xu, Z., Yu, P., Yi, J., & Xu, X. (2025). A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit. arXiv preprint arXiv:2502.09097.
  37. Wang, J., Xu, X., Yu, P., & Xu, Z. (2025). Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media.
  38. Huang, T., Yi, J., Yu, P., & Xu, X. (2025). Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies.
  39. Chen, Y., Feng, E., & Ling, Z. (2024). Secure Resource Allocation Optimization in Cloud Computing Using Deep Reinforcement Learning. Journal of Advanced Computing Systems, 4(11), 15-29.
  40. Xiong, K., Wu, Z., & Jia, X. (2025). DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments. Journal of Advanced Computing Systems, 5(1), 1-17.