A comparative study of machine learning-based regression models for supply chain management

Abstract

The rise of machine learning technology has opened up unprecedented opportunities for the retail industry. Machine learning, as an essential branch of artificial intelligence, enables computers to improve their performance through continuous learning and experience. It has demonstrated its ability to handle large-scale data and complex problems effectively. In retail, machine learning predictions and methods can also lead to significant breakthroughs in supply chain management, helping businesses identify better ways to maintain economic stability and growth, which are crucial for improving people's living standards, eliminating poverty, promoting social stability, driving technological progress, and reducing inequality. This is achieved through different algorithmic regression methods, which can predict future trends and consumer behavior with high accuracy. Machine learning algorithms can analyze vast amounts of data to identify patterns and trends and make accurate predictions about future demand, product inventory levels, and other important factors that drive business success in the retail industry.