Author(s)

Jie Li ¹

Affiliation(s)

¹ Zhejiang Hailiang Co., Ltd., Zhuji, Zhejiang, 311800, China

Corresponding Author

Jie Li

ABSTRACT

Aiming at the problems existing in traditional supply chain management, such as inaccurate demand forecast, low efficiency of inventory management and frequent adjustment of production plan, this study proposes an intelligent manufacturing supply chain management system solution based on ML (Machine Learning) technology and DNN (Deep Neural Network). In terms of methods, this article first carries out detailed data processing and feature engineering, and extracts key features from sales, production, inventory, suppliers and logistics. Then, the demand forecasting model based on LSTM (Long Short-Term Memory), inventory classification and supplier evaluation model based on DNN and logistics path optimization model based on CNN (Convolutional Neural Network) are constructed. The effectiveness and practicability of the system are verified by experiments. The results show that the system significantly improves the accuracy of demand forecasting, optimizes inventory management and production planning, and improves supplier management and logistics efficiency. Specifically, the accuracy of demand forecasting has increased by more than 19%, inventory turnover rate has increased by 31.5%, production efficiency has increased by 21%, supplier performance score has increased by 10.5%, and logistics transportation time has been shortened by 15%. These improvements have reduced the operating costs of enterprises and improved their market competitiveness and customer satisfaction.

KEYWORDS

Machine Learning; Intelligent Manufacturing; Supply chain management; Demand forecast; Logistics optimization

CITE THIS PAPER

Jie Li, Information Technology of Intelligent Manufacturing Supply Chain Management Based on Machine Learning. Industrial Engineering and Innovation Management (2025) Vol. 8: 68-76. DOI: http://dx.doi.org/10.23977/ieim.2025.080109.

REFERENCES

[1] Huang M, Yi P, Shi T, et al. A modal interval based method for dynamic decision model considering uncertain quality of used products in remanufacturing[J]. Journal of Intelligent Manufacturing, 2018, 29(4):925-935.
[2] Alavidoost M H, Tarimoradi M, Zarandi M H F. Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks[J]. Journal of Intelligent Manufacturing, 2018, 29(4):809-826.
[3] Jinliang C, Guoli L, Yu W. Enhance or impair: how does downstream complexity influence supply chain resilience with reconciling effects of intelligent manufacturing?[J]. Journal of Manufacturing Technology Management, 2023, 34(8):1356-1376.
[4] Gupta V, Jayant A, Singh K, et al. Implementation of Low Carbon Supply Chain Management Practices (LCSCMP) in Indian Manufacturing Industries using ISM-DEMATEL[J]. Journal of Advanced Manufacturing Systems, 2024, 23(04):985-1006.
[5] Abdullahi H O, Mohamud I H. The Impact of ICT on Supply Chain Management Efficiency and Effectiveness: A Literature Review[J]. Journal Europeen des Systemes Automatises, 2023, 56(2):309-315.
[6] Ji Z, Peigen L, Yanhong Z, et al. Toward New-Generation Intelligent Manufacturing[J]. Engineering, 2018, 4(4):11-20.
[7] Lu S, Liu S, Zhu Y, et al. A drl-based decentralized computation offloading method: An example of an intelligent manufacturing scenario[J]. IEEE Transactions on Industrial Informatics, 2022, 19(9): 9631-9641.
[8] Wang S, Meng J, Xie H S X. Reference training system for intelligent manufacturing talent education: platform construction and curriculum development[J]. Journal of Intelligent Manufacturing, 2023, 34(3):1125-1164.
[9] Wang Q, Chen H, Qiao L, et al. Path planning for UAV/UGV collaborative systems in intelligent manufacturing[J]. IET Intelligent Transport Systems, 2020, 14(11): 1475-1483.
[10] Cong Y, Tian D, Feng Y, et al. Speedup 3-D texture-less object recognition against self-occlusion for intelligent manufacturing[J]. IEEE transactions on cybernetics, 2018, 49(11): 3887-3897.
[11] Wang Z H, Li Y T, Wu Y C. Design of intelligent manufacturing IoT sensing system for polymer process monitoring[J]. The International Journal of Advanced Manufacturing Technology, 2023, 129(7): 2933-2947.
[12] Manjula R, Chauhan N. A secure and trusted consensus protocol for blockchain-enabled supply chain management system[J]. Peer-to-Peer Networking and Applications, 2024, 17(6):3815-3840.

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