Graph Neural Poisson Models for Supply Chain Relationship Forecasting

In supply chain networks, firms dynamically form or dissolve partnerships to adapt to market fluctuations, posing a challenge for predicting future supply relationships. We model the occurrence of supply edges (firm i to firm j) as a non-homogeneous Poisson process (NHPP), using historical event counts to estimate the Poisson intensity function up to time t. However, forecasting future intensities is hindered by the limitations of historical data alone. To overcome this, we propose a novel Graph Double Exponential Smoothing (GDES) model, which integrates graph neural networks (GNNs) with a nonparametric double exponential smoothing approach to predict the probability of future supply edge formations.Recognizing the interdependent economic dynamics between upstream and downstream firms, we assume that the Poisson intensity functions of supply edges are correlated, aligning with the non-homogeneous nature of the process.Our model is interpretable, decomposing intensity increments into contributions from the current edge's historical data and influences from neighboring edges in the supply chain network. Evaluated on a large-scale supply chain dataset with 87,969 firms, our approach achieves an AUC of 93.84 % in dynamic link prediction, demonstrating its effectiveness in capturing complex supply chain interactions for accurate forecasting.