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Top1. Introduction
Supply chain management is a crucial aspect of modern enterprise operations, and demand forecasting and inventory optimization are key issues within this domain. Accurate demand forecasting can help businesses plan production and inventory more effectively, avoiding situations of stockouts or excess inventory, thus improving customer satisfaction and operational efficiency(Sharma, 2020). However, traditional statistical methods have limitations in demand forecasting and inventory optimization due to the complexity of demand patterns and the challenges posed by large-scale data. Therefore, the application of deep learning and machine learning models is highly significant in addressing these problems.
The application of deep learning and machine learning models in supply chain management contributes to improving the accuracy of demand forecasting and the effectiveness of inventory optimization. By leveraging large-scale data and complex models, these methods can capture demand patterns, trends, and nonlinear relationships, thereby providing more precise predictions and optimized inventory management strategies. This is of great importance to businesses as it can reduce costs, enhance operational efficiency, and provide reliable decision-making support.
In the field of supply chain management, various deep learning and machine learning models are widely employed. Here are five common models and their advantages and disadvantages: Recurrent Neural Networks (RNN) are models suitable for sequence data modeling, capable of capturing temporal dependencies(Bandara, 2019). However, RNNs suffer from the problem of vanishing or exploding gradients when dealing with long-term dependencies and memory. Long Short-Term Memory (LSTM) is an improved variant of RNN that addresses the issues of vanishing and exploding gradients by introducing gate mechanisms(Bandara, 2019). It performs well in handling long-term dependencies and memory, but it has higher computational complexity when dealing with large-scale data. Convolutional Neural Networks (CNN) are primarily used for image processing and are effective in extracting spatial features. In demand forecasting, CNN can be used to extract spatiotemporal features from demand data(Name, 1996). However, CNN’s modeling capability for sequence data is relatively weak. Self-Attention Mechanism is a model that captures dependencies at different positions in a sequence. It can effectively learn important information in the sequence for demand forecasting, but it has higher computational complexity when dealing with long sequences. Random Forest is an ensemble learning method that makes predictions by combining multiple decision trees. It performs well in handling large-scale data, but its modeling capability for complex nonlinear relationships is relatively weak.
This study aims to enhance the effectiveness of demand forecasting and inventory optimization in supply chain management. Traditional methods have limitations when dealing with complex demand patterns and large-scale data, so the introduction of deep learning models is highly significant in addressing these issues. Additionally, the combination of Bayesian optimization, CNN, and LSTM can fully leverage large-scale data and powerful modeling capabilities to improve the accuracy and efficiency of demand forecasting and inventory optimization. This study proposes a method based on BO-CNN-LSTM to enhance the effectiveness of demand forecasting and inventory optimization in supply chain management(Kiuchi, 2020). The main principles of this method are as follows:
Firstly, Bayesian optimization is employed to automatically tune the hyperparameters of the model to achieve optimal performance. Bayesian optimization progressively optimizes the selection of hyperparameters by continually exploring and exploiting model performance feedback, thereby improving the model’s performance and generalization ability.