GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain Networks

TL;DR

This paper introduces a GNN-based probabilistic model that enhances supply and inventory predictions in complex supply chain networks, leading to more accurate and reliable supply planning.

Contribution

The paper presents the GSP model, an attention-based graph neural network that predicts supplies, inventory, and imbalances using graph-structured data, improving supply chain prediction accuracy.

Findings

GSP significantly improves supply and inventory prediction accuracy.

The model enables supply plan corrections for better execution.

Experiments on real-world data validate the model's effectiveness.

Abstract

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.