Article Preview
Top1. Introduction
Supply chain management is a crucial component of modern business operations, aiming to improve efficiency, reduce costs, and ensure product quality[18]. With the growing volume and complexity of supply chain data, the application of artificial intelligence techniques such as deep learning and machine learning in supply chain management has gained increasing attention[19]. This article provides an overview of deep learning and machine learning models in supply chain management and proposes a new method to address the challenges in this field. Supply chain management involves multiple stages and stakeholders, including suppliers, manufacturers, logistics companies, and more. Effectively managing and optimizing the supply chain can help businesses reduce inventory costs, improve delivery speed, mitigate supply chain risks, and enhance customer satisfaction[20]. Deep learning and machine learning models can leverage hidden patterns and insights in supply chain data to provide accurate predictions and decision support, thus enhancing supply chain visibility, efficiency, and reliability. The following are several commonly used machine learning models:
1.1 Convolutional Neural Networks (CNN)
Convolutional Neural Networks[21] excel in processing images and spatial data. They can automatically extract features from images through convolutional and pooling layers, enabling local perception and feature extraction. CNNs are widely used in computer vision tasks such as image classification, object detection, and image segmentation. However, CNNs are not suitable for handling sequential data or data with long-term dependencies since they cannot capture the temporal relationships between data.
1.2 Long Short-Term Memory (LSTM)
Long Short-Term Memory[22] is a type of recurrent neural network (RNN) that is well-suited for handling sequential data. LSTM effectively captures long-term dependencies in time series data through the design of gated units. Compared to traditional RNNs, LSTM performs better in addressing issues like vanishing gradients and exploding gradients, making it more suitable for processing long sequences of data. However, LSTM has poorer performance in handling spatial data and images because it cannot effectively extract spatial features.
1.3 Generative Adversarial Networks (GAN)
Generative Adversarial Networks[23] consist of a generator and a discriminator and can generate realistic synthetic data. GANs are widely used in tasks such as image generation, image enhancement, and data augmentation. The generator learns to generate synthetic data by learning the distribution of real data, while the discriminator is trained to distinguish between real and synthetic data. GANs have strong generative capabilities and can generate high-quality synthetic data, making them advantageous for data augmentation and filling missing data. However, the training process of GANs is relatively unstable and requires a large amount of data and computational resources.
1.4 Support Vector Machines (SVM)
Support Vector Machines[24] are commonly used supervised learning algorithms suitable for binary and multi-class classification problems. SVM maps data to a high-dimensional feature space and finds the optimal hyperplane for classification in that feature space. SVM performs well in handling small-sample and high-dimensional data, effectively dealing with linearly separable and nonlinearly separable problems. However, SVM has lower computational efficiency when dealing with large-scale datasets and limited capabilities in handling sequential data.