GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain
  Networks
Hyung-il Ahn Young Chol Song Santiago Olivar Hershel Mehta Naveen Tewari
Successful supply chain optimization must mitigate imbalances between supply
and demand over time. While accurate demand prediction is essential for supply
planning, it alone does not suffice. The key to successful supply planning for
optimal and viable execution lies in maximizing predictability for both demand
and supply throughout an execution horizon. Therefore, enhancing the accuracy
of supply predictions is imperative to create an attainable supply plan that
matches demand without overstocking or understocking. However, in complex
supply chain networks with numerous nodes and edges, accurate supply
predictions are challenging due to dynamic node interactions, cascading supply
delays, resource availability, production and logistic capabilities.
Consequently, supply executions often deviate from their initial plans. To
address this, we present the Graph-based Supply Prediction (GSP) probabilistic
model. Our attention-based graph neural network (GNN) model predicts supplies,
inventory, and imbalances using graph-structured historical data, demand
forecasting, and original supply plan inputs. The experiments, conducted using
historical data from a global consumer goods company's large-scale supply
chain, demonstrate that GSP significantly improves supply and inventory
prediction accuracy, potentially offering supply plan corrections to optimize
executions.