Resilience Inference for Supply Chains with Hypergraph Neural Network

TL;DR

This paper introduces a hypergraph neural network model to infer supply chain resilience from network topology and inventory data, enabling proactive risk management without explicit dynamic models.

Contribution

It formalizes the Supply Chain Resilience Inference problem and proposes the SC-RIHN model that captures multi-entity dependencies using hypergraph message passing.

Findings

SC-RIHN outperforms traditional models on synthetic benchmarks.

The model effectively captures multi-party interactions in supply chains.

Results indicate potential for early-warning risk assessment.

Abstract

Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate and timely inference of supply chain resilience the capability to maintain core functions during disruptions is crucial for proactive risk mitigation and robust network design. However, existing approaches lack effective mechanisms to infer supply chain resilience without explicit system dynamics and struggle to represent the higher-order, multi-entity dependencies inherent in supply chain networks. These limitations motivate the definition of a novel problem and the development of targeted modeling solutions. To address these challenges, we formalize a novel problem: Supply Chain Resilience Inference (SCRI), defined as predicting supply chain resilience using hypergraph topology and observed inventory trajectories without explicit dynamic equations. To solve this problem, we propose the Supply Chain Resilience Inference Hypergraph Network (SC-RIHN), a novel hypergraph-based model leveraging set-based encoding and hypergraph message passing to capture multi-party firm-product interactions. Comprehensive experiments demonstrate that SC-RIHN significantly outperforms traditional MLP, representative graph neural network variants, and ResInf baselines across synthetic benchmarks, underscoring its potential for practical, early-warning risk assessment in complex supply chain systems.