Demand Forecasting for Inventory Optimization
Project Overview
Developed and implemented advanced machine learning models to forecast product demand for an e-commerce platform. The forecasting system was designed to optimize procurement decisions and inventory management, significantly reducing costs associated with excess inventory while maintaining appropriate stock levels.
Methodology
Utilized historical sales data, seasonal patterns, promotional events, and external factors to build predictive models. Implemented a combination of time series analysis techniques (ARIMA, SARIMA) and machine learning approaches (XGBoost, LSTM networks) to capture both linear and non-linear patterns in the data. Created an ensemble model that combined the strengths of multiple approaches for optimal prediction accuracy.
Results
The forecasting models achieved a 25% improvement in prediction accuracy compared to previous methods. This enhanced accuracy translated directly to business value, reducing excess inventory costs by 15-20% while maintaining service levels and customer satisfaction.
Conclusion
The project demonstrated the significant business impact of advanced forecasting techniques in inventory management. By accurately predicting future demand, the company was able to optimize its procurement strategy, reducing costs while ensuring product availability.