Learning production functions for supply chains with graph neural networks

TL;DR

This paper introduces a novel graph neural network model that combines temporal GNNs with an inventory module to accurately infer production functions and forecast transactions in supply chain networks, enhancing supply chain visibility.

Contribution

The paper presents a new GNN-based model with an inventory module that captures hidden production functions in supply chains, outperforming existing methods on real and simulated data.

Findings

Successfully infers production functions with 6%-50% improvement over baselines.

Achieves 11%-62% better accuracy in forecasting future transactions.

Demonstrates effectiveness on real supply chain data and a new simulator.

Abstract

The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to improve supply chain visibility and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture these hidden relationships between nodes' inputs and outputs. Here, we introduce a new class of models for this setting by combining temporal GNNs with a novel inventory module, which learns production functions via attention weights and a special loss function. We evaluate our models extensively on real supply chains data and data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, outperforming the strongest baseline by 6%-50% (across datasets), and forecast future transactions, outperforming the strongest baseline by 11%-62%