Time Series Isn’t Enough: How Graph Neural Networks Change Demand Forecasting

Traditional demand forecasting often fails because it treats products in isolation, ignoring that a supply chain is a connected network where one item's demand impacts others. Graph Neural Networks (GNNs) offer a powerful alternative by modeling products and their relationships—like shared factories or product groups—as an interconnected system. This approach allows each product's forecast to be influenced not just by its own sales history but also by real-time signals from its connected neighbors