Dynamic Demand Forecasting with AI: Maximizing Supply Chain Value

This paper presents the development of a dynamic demand forecasting platform designed to predict demand accurately and quantify its added value in supply chain management using advanced machine learning and operations management algorithms. The platform collects and processes real-time data from multiple sources, including point-of-sale systems, e-commerce transactions, competitor pricing, weather conditions, and consumer sentiment data. By integrating environmental and product-specific factors, the platform optimizes inventory levels, delivery routing, and operational efficiency. It achieves superior predictive accuracy by leveraging models such as XGBoost and Support Vector Regression (SVR), which consistently outperform traditional methods. The solution is designed to be scalable, cost-effective, and user-friendly, making it particularly accessible to small and medium-sized enterprises (SMEs) that often struggle to implement advanced forecasting systems due to technological and financial constraints. The system's implementation significantly enhances decision-making processes, resulting in reduced operational costs and improved customer satisfaction.