Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

The article expects to solve the traditional econometric statistical model, shallow machine learning algorithm, and many limitations in learning the nonlinear relationship of related indicators affecting commodity futures price trend. This article proposes a neural network commodity futures price prediction model by the mixture of convolutional neural networks (CNN) and gated recurrent unit (GRU). Firstly, the dimension reduction algorithm of multidimensional data by principal component analysis (PCA) is used. Through linear transformation, the original variables with correlation are transformed into a set of new linear irrelevant variables, and the high-dimensional time series data of commodity futures are reduced. Secondly, the variable features are extracted from the CNN network module in the CNN-GRU model, and the GRU network module learns the periodicity and trend of the original data. Finally, the full connection layer outputs the forecast results of commodity futures price.