Intelligent Optimization of Material Supply Chain via Machine Learning

The primary task of intelligent optimization of the entire supply chain is to address the bullwhip effect, and improving demand forecasting accuracy can effectively suppress the bullwhip effect. Building a model with high accuracy in demand forecasting for the material supply chain provides an important theoretical basis for enterprises to respond to market demand changes. With the significant advancements in machine learning technology, predictive analytics has gradually become a research hotspot. Improving the accuracy of forecasting helps to grasp future economic trends and consumer trends. This paper predicts the demand of the material supply chain based on deep learning methods, essentially a multivariate time series forecasting module. Firstly, the relevant data is processed through feature engineering, and a prediction model consisting of a CNN for extracting stationary features and an LSTM for analyzing time series is constructed. To verify the effectiveness of the model, this paper compares it with traditional machine learning algorithms and classical neural networks. The experiments demonstrate that the proposed method in this paper achieves good performance in demand forecasting for the material supply chain.