Exploring the Effectiveness of Ensemble Learning for Business-to-Business (B2B) Demand Forecasting: An Empirical Analysis

Accurate demand forecasting is a critical component of supply chain management, particularly in Business-to-Business (B2B) settings where inventory management and production planning are essential for maintaining operational efficiency and customer satisfaction. This study explores the application of ensemble learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Random Forest, for enhanced B2B demand forecasting. The performance of the ensemble methods is evaluated against the traditional SARIMA (Seasonal Autoregressive Integrated Moving Average) and Holt-Winters Exponential Smoothing (HWES) model. The methodology utilizes a comprehensive dataset of transactional records and involves rigorous data preprocessing, including the creation of custom variables to augment model relevance. The ensemble learning approach demonstrates superior performance compared to the traditional methods, as evidenced by lower Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Weighted Mean Absolute Percentage Error (WMAPE) values. These findings highlight the potential of ensemble learning techniques in refining B2B demand forecasts, providing businesses with more accurate and reliable insights to optimize supply chain strategies and enhance operational efficiency.