Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

• Published in: Journal of intelligent & fuzzy systems Vol. 40; no. 4; pp. 7013 - 7024
• Main Author: Lu, Shaoqin
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2021; Sage Publications Ltd
• Subjects: Algorithms; Decision making; Inventory; Inventory control; Machine learning; Neural networks; Relocation; Risk assessment; Supply chains; risk assessment; machine learning; neural network; enterprise supply chain; Improved algorithm
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-189532

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.