Modeling on disruption risk prediction of manufacturing supply chain based on c4.5 algorithm

Abstract

Under the impact of covid-19, the global and domestic manufacturing supply chains, almost suffered from the serious interruption crisis of manpower flow, logistics, information flow and capital flow. The risk of supply chain disruption has become the primary risk of the supply chain. However, some risk inducement of supply chain interruption is complex and diverse, so it is very difficult to grasp and screen the risk data needed for research from the supply chain operation data. To improve the robustness of supply chain for boosting the domestic and international circulation of China's manufacturing, in this paper, according to the characteristics of China's manufacturing supply chain and its risk incentives, the data needed for risk prediction modeling has been sorted out through questionnaire survey, and a regression model of risk prediction for manufacturing supply chain by using empirical method would be put forward. Then, C4.5 decision tree method is used to train and evaluation the risk prediction model. The conclusion shows that the customer satisfaction has great diagnostic value for risk, and the model has a strong sensitivity to market information risk and market order risk. The conclusion is more consistent with general cognition, and the model fits well, indicating that the model proposed in this paper has a certain theoretical significance, and its practical application value is worthy of further testing.