Intelligent Method of Supply Chain Circulation Industry Structure Based on Machine Learning

Abstract

In the deepening of supply chain competition, whether the structure of supply chain industry is reasonable and scientific has been severely tested. For warehousing, purchase and distribution channels, and customers, it largely determines whether the structure of supply chain is stable and efficient. The rationality of structure can determine the value of supply chain. By analyzing these four levels, this paper judges whether the supply chain structure is reasonable; the judgment standard is based on the three popular machine learning models, Stochastic Forest, XGBoost, and Support Vector Machine. The three models are based on a large number of real data environments. Through data simulation and parameter optimization, four supply chain characteristics are put into the model for simulation training for many times, and the three error numbers of MAE, RMSE, and MAPE of the model are analyzed to judge the reliability of the model. On this basis, through the combination of models, it is determined that the average percentage error of the combination of the three models is higher than that of the other pairwise combinations, reaching 0.937, which completes the expectation of intelligent prediction of supply chain structure.