Integrating LLMs into AI-Driven Supply Chains: Best Practices for Training, Development, and Deployment in the Retail and Manufacturing Industries
Keywords:
Large Language Models, AI-driven supply chainsAbstract
In AI-driven supply chains, LLMs increase retail and industrial decision-making and efficiency. We examine supply chain LLM training, development, and implementation best practices to improve demand forecasting, supplier risk management, logistics automation, and other vital activities. LLMs create natural language from human data. The complexity and amount of data in contemporary supply networks has enhanced SCM value. Supplier risk, demand forecasting, logistical automation, and resilience improve with LLMs.
The study opens by contextualizing AI's rise in retail and industrial supply chains and LLMs' importance. LLMs manage inventory and demand using market trends, consumer input, and news. Operations fit AI/LLM supply chains. For scalability, robustness, and industry standards, data pretreatment, model selection, and supply chain LLM fine-tuning follow.
References
J. Devlin, M.-T. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv preprint arXiv:1810.04805, 2018.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving Language Understanding by Generative Pre-Training,” OpenAI, 2018.
T. Wolf, L. Chaumond, and J. Debut, “Transformers: State-of-the-Art Natural Language Processing,” arXiv preprint arXiv:1910.03771, 2019.
K. Chen, J. L. Li, and M. Zhang, “Deep Learning for Supply Chain Management: A Review and Future Directions,” IEEE Access, vol. 8, pp. 152885-152903, 2020.
S. K. Bansal, N. N. Dufour, and S. K. Sahu, “Leveraging Machine Learning for Enhanced Demand Forecasting,” Journal of Operations Management, vol. 66, no. 1, pp. 15-28, 2021.
T. Zhang, X. Huang, and Y. Zhao, “A Survey of Machine Learning Approaches for Risk Management in Supply Chains,” Computers & Industrial Engineering, vol. 145, pp. 106533, 2020.
D. F. Silva, A. B. Nogueira, and L. C. M. Silva, “Automating Logistics with Deep Learning: Applications and Challenges,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 4773-4785, 2021.
Z. Zhang, L. Sun, and J. Liu, “End-to-End Learning for Logistics Optimization Using Deep Reinforcement Learning,” Proceedings of the IEEE International Conference on Big Data (Big Data), pp. 1-10, 2020.
J. Gao, H. Liu, and Y. Wang, “Data Privacy and Security Challenges in AI-Driven Supply Chains,” IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2672-2684, 2021.
M. D. Smith and J. B. Yates, “Scalability of Machine Learning Models in Supply Chain Applications,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 1492-1502, 2020.
A. Patel and R. S. Singh, “Domain Adaptation Techniques for Fine-Tuning Large Language Models,” Journal of Machine Learning Research, vol. 21, no. 1, pp. 1-25, 2020.
C. Xu, J. Lee, and P. Zhao, “Optimizing Supply Chains with Neural Networks: A Review,” IEEE Transactions on Engineering Management, vol. 67, no. 2, pp. 249-260, 2020.
H. Kim, Y. H. Lee, and S. J. Park, “Leveraging AI for Enhanced Supply Chain Visibility and Performance,” Computers & Industrial Engineering, vol. 150, pp. 106458, 2021.
Y. Zhang, L. Chen, and X. Zhang, “Large Language Models in Industry: Applications and Challenges,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 123-137, 2022.
M. H. B. Hassoun, “Deep Learning in Supply Chain Management: Methods and Applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 1592-1605, 2021.
J. Huang and L. Wang, “Advanced Techniques for Training Large Language Models: A Review,” IEEE Access, vol. 9, pp. 20025-20039, 2021.
K. Wright, S. Kim, and T. Anderson, “Evaluating AI Model Performance: Metrics and Techniques,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 9, pp. 3189-3202, 2021.
P. R. Kumar, A. S. Bhat, and N. G. Bansal, “Integrating AI Technologies with Existing Supply Chain Systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 2, pp. 114-126, 2021.
L. Zhang, M. Liu, and R. Gupta, “Interpretability in AI Models for Supply Chain Management,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1053-1066, 2022.
S. Mohan, V. S. K. Babu, and R. Jain, “Future Directions in AI for Supply Chain Management,” IEEE Transactions on Automation Science and Engineering, vol. 18, no. 2, pp. 745-756, 2021.
