Enhancing Inventory Forecasting Accuracy and Optimization Using Machine Learning

As enterprises increasingly prioritize refined inventory management, the application of machine learning for inventory forecasting and optimization has become increasingly critical. This paper systematically discusses the application of machine learning models in inventory management systems, and focuses on the analysis of ETL integration, model deployment, real-time learning, and other key technologies in the system. This paper explores methods to enhance the accuracy of inventory forecasting from three key perspectives: constructing time series model, exogenous influence and comparison of important methods. The forecast results are applied to variable safety inventory, intelligent replenishment strategy and inventory structure optimization respectively, and an inventory optimization plan based on the forecast results is constructed. The research findings offer valuable theoretical and technical insights for enterprises to enhance inventory management efficiency.