ABC-XYZ Classification and Forecasting for Inventory Optimization

Effective inventory management remains a significant challenge for businesses facing fluctuating demand and seasonal variations.Traditional methods often fall short, leading to issues such as stockouts, overstocking, and inefficient resource use.This study presents an integrated approach to inventory management by combining ABC-XYZ classification with time series forecasting using machine learning techniques.The ABC-XYZ analysis categorizes inventory items based on their revenue significance and demand variability, enabling a more nuanced understanding of inventory prioritization.By leveraging historical sales data, we apply Random Forest Regression to forecast future demand for different item categories, providing actionable insights for optimizing inventory levels.This approach not only enhances the accuracy of inventory predictions but also supports more efficient resource allocation, ultimately reducing stockouts and overstock situations.The findings demonstrate the potential of advanced analytics in streamlining inventory management processes and improving overall supply chain efficiency.