Full-Chain Intelligent Supply Decision-Making Model: Accurate Prediction and Decision Support Based on Deep Learning Algorithms

In response to the many challenges faced by current supply chain management, such as market demand volatility, supply network vulnerabilities, escalating transportation expenses, and issues including flawed forecasting and inadequate analytical support in conventional supply chain systems, this study introduces an end-to-end cognitive supply optimization framework leveraging neural network architectures. First, this paper uses edge computing technology to filter and preliminarily analyze front-end data, and based on accurate data support, builds a full-chain intelligent supply decision model based on deep learning algorithms, thereby improving the intelligence level of supply chain management and helping enterprises reduce costs. In the data preprocessing stage, this paper adopts data cleaning, missing value filling and data standardization. In the feature extraction stage, this paper uses convolutional neural network (CNN) and recurrent neural network (RNN) in deep learning algorithms to extract features and recognize patterns of data in various links of the supply chain. In the model training stage, this paper adopts cross-validation, grid search and regularization strategies to optimize and adjust the hyperparameters of the model, thereby improving the generalization ability and robustness of the model. Finally, this paper verifies the effectiveness of the model through experiments. The experimental results show that the accuracy of the full-chain intelligent supply chain management algorithm is stably maintained between 70% and 90% under most test time numbers, and the overall performance is more balanced and robust, demonstrating its achievements in practical applications. This research result not only provides a new intelligent solution for supply chain management but also provides strong support for the sustainable development of enterprises.