Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Journal of Artificial Intelligence Practice
  4. Vol 8, Issue 2, 2025

Construction and Practice of Supply Chain Optimization Decision System Driven by Artificial Intelligence

Download as PDF

DOI: 10.23977/jaip.2025.080207 | Downloads: 21 | Views: 521

Author(s)

Jie Li 1

Affiliation(s)

1 Zhejiang Hailiang Co., Ltd., Zhuji, Zhejiang, 311814, China

Corresponding Author

Jie Li

ABSTRACT

This article focuses on the supply chain optimization decision-making system driven by artificial intelligence (AI), aiming at coping with the complex challenges faced by enterprise supply chain management and improving its operational efficiency and competitiveness. Through the combination of theoretical research and case practice, this article first expounds the theoretical and technical basis of supply chain management, AI and decision-making system. On this basis, the system is constructed in detail, covering demand analysis, architecture design and key module design, in which the key module adopts demand forecasting method combining time series with neural network, inventory optimization strategy based on EOQ model and transportation scheduling scheme of genetic algorithm. The practice in large-scale electronic product manufacturing enterprises shows that after the system is implemented, the inventory turnover rate of enterprises increases by 37.14%, the shortage rate decreases by 62.20%, and the transportation cost decreases by 18.33%. The research shows that the AI-driven supply chain optimization decision-making system can effectively solve the supply chain management problems of enterprises, which is of great value to improve the operational efficiency of enterprises, but some models still need to be optimized when dealing with extreme market changes.

KEYWORDS

Artificial intelligence; Supply chain optimization; Decision system; Machine learning; Bullwhip effect

CITE THIS PAPER

Jie Li, Construction and Practice of Supply Chain Optimization Decision System Driven by Artificial Intelligence. Journal of Artificial Intelligence Practice (2025) Vol. 8: 54-60. DOI: http://dx.doi.org/10.23977/jaip.2025.080207.

REFERENCES

[1] Li Xiang, Wang Meiqi, Guo Chang. Research on the Decision Optimization of a Dual-channel Supply Chain Considering the Service Level of Physical Stores. Chinese Journal of Management, vol. 21, no. 8, pp. 1217-1226, 2024.
[2] Zhang Mingqiang, Gao Hua, Yuan Dongfeng, et al. Design and Implementation of an Artificial Intelligence Detection System for Appearance Quality with Cloud-edge Collaboration. Computer Integrated Manufacturing Systems, vol. 29, no. 10, pp. 3440-3449, 2023.
[3] Song Hua. Theoretical Exploration and Prospect of the Digital Supply Chain with Artificial Intelligence. China Business and Market, vol. 38, no. 1, pp. 44-54, 2024.
[4] Xin Daleng, Qiu Yue. Artificial Intelligence, Stability of the Industrial and Supply Chains, and the Export Resilience of Enterprises. Economic Theory and Business Management, vol. 45, no. 2, pp. 37-54, 2025.
[5] Wu Jun, Zhang Lei. Master-slave Interactive Optimization of Product Family Architecture Design and Supply Chain Delay Decision. Computer Integrated Manufacturing Systems, vol. 30, no. 10, pp. 3719-3729, 2024.
[6] Qian Wuyong, Zhang Haonan. Research on the Credit Risk Evaluation of Supply Chain Finance Based on the Adaboost-DPSO-SVM Model. Journal of Industrial Technological Economics, vol. 41, no. 3, pp. 72-79, 2022.
[7] Wang Hui, Wang Junjie, Wang Tengfei, et al. Optimal Decision of the Wind Power Supply Chain Based on CVaR under Uncertain Output. Journal of Electric Power Science and Technology, vol. 38, no. 4, pp. 134-142, 2023.
[8] Wang Jingjing, Xu Minli. Research on the Decision-making of a Closed-loop Supply Chain Considering Echelon Advantages under the Extended Producer Responsibility System. Journal of Industrial Engineering and Engineering Management, vol. 38, no. 4, pp. 222-238, 2024.
[9] Qiu Ruozhen, Wu Xu, Sun Yue, et al. A Robust Optimization and Coordination Model of the Supply Chain Considering Big Data Investment Decision under Uncertain Demand. Chinese Journal of Management Science, vol. 31, no. 1, pp. 128-141, 2023.
[10] Qiu Ruozhen, Chu Xiaojing, Sun Yue. A Robust Optimization-based Decision-making Model for a Dual-channel Supply Chain under Price and Delivery Time-sensitive Demand. Chinese Journal of Management Science, vol. 31, no. 9, pp. 114-126, 2023.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 15023
Visits: 473670

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.