Predictive Models for Inventory Optimization: a machine learning application for demand forecasting at a construction supplies distributor

Inventory management is a critical task for distribution companies, as forecasting errors can lead to excess inventory, higher storage costs, or stockouts. This practice & policy paper describes the development and deployment of a machine learning-based predictive model designed to optimize inventory management for a medium-sized Brazilian construction supplies distributor. Using the Amazon Forecast and Amazon SageMaker Canvas platforms, the study demonstrated how advanced demand forecasting techniques reduce stockouts and enhance operational efficiency. The research compared various predictive algorithms, assessing their performance with metrics such as RMSE, MAPE, MASE, and WAPE. The results showed that the implemented model achieved approximately 99.31% forecast accuracy, offering significant benefits like fewer stockouts, optimized inventory levels, and improved purchasing planning. This practice study provides technical and managerial recommendations for companies seeking to predict their inventory needs accurately, demonstrating the machine learning capabilities for Supply Chain Management.