Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System

Authors

  • Jie Wei Business Management Department, ChongQing Vocational College of Transportation, Jiangjin 402247, Chongqing, China

Abstract

With the rapid development of the market economy and the manufacturing industry, the market has already reached saturation. The fierce competition between enterprises is reflected in their products and services, which is fundamentally a matter of the competition between supply chain management modes. In recent years, artificial intelligence (AI) has developed rapidly. The application of human-computer interaction technology to supply chain management (SCM) systems can improve the efficiency of SCM. Although the traditional SCM mode is adequate for product manufacturing, transportation and sales, it is unable to rapidly customize the customer’s product demand. The traceability function is a production control system that can track companies’ products. This paper compared the collaboration mode of agile SCM based on AI traceability system with the traditional
SCM mode. The experimental results showed that the responsiveness of the traditional SCM mode and the agile SCM collaboration mode based on AI traceability system were 76% and 89.8% respectively in a small supply chain. In large supply chains, the responsiveness of the traditional SCM mode and the agile SCM collaboration mode based on AI traceability system were 70.8% and 87.6% respectively. Therefore, human-computer interaction in the collaboration mode of agile SCM based on AI traceability system can improve the responsiveness of the supply chain to customer needs.

Keywords: Supply chain (SC), agile supply chain, manage collaboration, traceability system, artificial intelligence, multimodal learning

Cite As

J. Wei, "Agile Supply Chain Management Collaboration Based on Artificial Intelligence Traceability System", 
Engineering Intelligent Systems, vol. 32 no. 5, pp. 401-410, 2024.





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Published

2024-09-01

Issue

Vol. 32 No. 5 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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