Neural Networks for Supply Chain Demand Forecasting

Abstract

Accurate demand forecasting is critical for optimizing supply chain operations, ensuring that products are available when needed while minimizing excess inventory. Neural networks, particularly deep learning models, have shown great promise in improving the accuracy of demand forecasting by analyzing large datasets and identifying complex patterns. This article explores the application of neural networks in supply chain demand forecasting, focusing on key techniques such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs). We discuss the benefits and challenges of using neural networks for demand forecasting, as well as their potential to transform supply chain management.