Predicting Demand in Supply Chain Management: A Decision Support System Using Graph Convolutional Networks

This work addresses the problem of demand forecasting in supply chain management, where the consolidation of scattered and heterogeneous data and the lack of precise forecasting methods generate operational inefficiencies, resulting in increased backorders and high inventory costs. To tackle these challenges, we propose a novel Decision Support System that jointly integrates an intelligent processing engine based on Graph Neural Networks (GNNs) for time series forecasting. Our approach lies in explicitly modeling the demand prediction task as a Multivariate Time Series forecasting problem on a causal dependency graph. Specifically, we use a GCN to process a graph where the nodes represent the target demand and key exogenous variables (Consumer Sentiment Index, Consumer Price Index, Personal Income, and Unemployment Rate), and the edges explicitly encode the interdependencies and causal relationships among these economic factors and demand. Unlike previous applications of GNNs in supply chain management, which typically focus on inventory networks or single-factor interactions, our approach uses GCN to dynamically capture the temporal interactions among multiple macroeconomic and internal series on future demand. We compare our method with other machine learning algorithms for demand forecasting. In the experiments conducted, the proposed GCN approach can accurately predict the abrupt changes that appear in demand behavior over time, whereas the other comparison methods tend to excessively smooth these transitions.