“Predictive modelling of demands: A machine learning approach for global network sales”

Accurate sales demand forecasting is essential for organizations operating in global networks, where dynamic market conditions, seasonal fluctuations, and changing customer preferences significantly influence business performance. Traditional forecasting techniques often struggle to capture complex nonlinear relationships within large-scale sales data, resulting in reduced prediction accuracy and inefficient inventory management. This study presents a machine learning-based predictive modelling framework designed to improve sales demand forecasting by leveraging historical sales records, customer behaviour, product attributes, and external market factors. The proposed approach employs advanced machine learning algorithms to identify hidden patterns and generate accurate demand predictions across geographically distributed markets. Comprehensive data preprocessing, feature engineering, and model optimization techniques are incorporated to enhance prediction performance while reducing noise and handling missing values. The forecasting models are evaluated using standard performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), demonstrating their effectiveness in comparison with conventional statistical forecasting methods. The proposed predictive framework offers a scalable and adaptable solution for global business environments, contributing to enhanced operational efficiency, customer satisfaction, and sustainable competitive advantage. This research highlights the growing importance of artificial intelligence and machine learning in modern sales demand forecasting and global supply chain management.