Leveraging Machine Learning for Inventory Optimization in American Retail Management

Abstract

Inventory optimization is a critical aspect of retail management, with efficient inventory management being of utmost importance for the overall performance of the supply chain. In recent years, the application of machine learning (ML) in inventory optimization has gained significant traction. ML models are being used for demand forecasting, supply chain performance improvement, and handling unexpected events in the supply chain. For instance, reinforcement learning (RL) algorithms have been increasingly adopted to enhance forecast accuracy and address supply chain optimization challenges. As highlighted by [1], companies like UPS and Amazon have developed RL algorithms to meet rising consumer delivery expectations, showcasing the practical relevance of ML in retail supply chain management. Additionally, [2] emphasizes the significance of utilizing raw data to improve efficiency in supply chains, particularly in addressing inventory management challenges. The availability of large datasets and computational power has facilitated the emergence of new ML algorithms to solve inventory optimization problems in data-rich environments.