Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models
Authors/Creators
- 1. Information Networking, Carnegie Mellon University, PA, USA
- 2. Business Analytics, Trine University, AZ, USA
- 3. Information Science, Trine University, Phoenix, AZ, USA
- 4. Information System & Technology Data Analytics, California State University, CA, USA
- 5. Artificial Intelligence, Royal Holloway University of London, Egham, UK
Description
This paper explores the application and advantages of large-scale AI models in logistics and supply chains. Traditional enterprises need help with the timely detection of anomalies in the supply chain. At the same time, AI algorithms can quickly identify abnormal patterns in the data and issue alerts, helping enterprises adjust real-time strategies to ensure the supply chain's stable operation. AI also reduces inventory costs and economic losses by predicting changes in market demand and optimizing inventory management. In addition, AI models perform well in intelligent scheduling and route planning, providing optimized solutions based on factors such as traffic flow, road conditions, and weather forecasts to improve transportation efficiency and accuracy. The article details the system architecture and functional modules designed to help enterprises meet the transformation challenges of the digital age.
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ASEJAR2024030401.pdf
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Additional details
References
- Wang, Y., Zhan, X., Zhan, T., Xu, J., & Bai, X. (2024). Machine learning-based facial recognition for financial fraud prevention. Journal of Computer Technology and Applied Mathematics, 1(1), 77-84.
- Wang, X., Tian, J., Qi, Y., Li, H., & Feng, Y. (2024). Short-term passenger flow prediction for urban rail transit based on machine learning. Journal of Computer Technology and Applied Mathematics,1(1), 63-69.
- Bai, Xinzhu, Wei Jiang, & Jiahao Xu. (2024). Development trends in AI-based financial risk monitoring technologies. Journal of Economic Theory and Business Management 1(2), 58-63.
- Ding, W., Zhou, H., Tan, H., Li, Z., & Fan, C. (2024). Automated compatibility testing method for distributed software systems in cloud computing.
- Qian, K., Fan, C., Li, Z., Zhou, H., & Ding, W. (2024). Implementation of artificial intelligence in investment decision-making in the chinese a-share market. Journal of Economic Theory and Business Management, 1(2), 36-42.