Research on Risk Identification of New Retail Supply Chain in the Context of Internet
Based on Machine Learning Algorithm
- DOI
- 10.2991/978-94-6463-222-4_41How to use a DOI?
- Keywords
- internet: digital economy; supply chain; risk identification; machine learning; BP neural network
- Abstract
With the development of the Internet, the overall growth rate of China's traditional retail industry has shown a downward trend, most of the physical stores are experiencing operational difficulties. After the new retail model was proposed, some traditional retail industries gradually tried digital retail transformation. Influenced by the Internet, digital has empowered the traditional retail industry, and the new retail supply chain has received more and more attention as an important chain connecting customers and retailers. Therefore, this paper focuses on the fresh new retail supply chain, which requires high supply chain risk control, and identifies the risks in the supply chain of fresh products in the context of the Internet through factor analysis and machine learning to provide a basis for fresh supply chain risk management decisions.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yuhua Li AU - Xiao Kong PY - 2023 DA - 2023/08/28 TI - Research on Risk Identification of New Retail Supply Chain in the Context of Internet BT - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023) PB - Atlantis Press SP - 381 EP - 389 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-222-4_41 DO - 10.2991/978-94-6463-222-4_41 ID - Li2023 ER -