Digital-Twin-Guided Protocol for Supply Chain Risk Prediction and Mitigation with Neural Sequence and Graph Models

Summary

This protocol details a reproducible digital-twin workflow integrating data ingestion, sequence-and-graph-based risk prediction, propagation simulation, and policy optimization. It specifies software versions, parameters, checkpoints, and troubleshooting steps to support faithful replication in industrial settings.

Abstract

Supply chains require real-time risk prediction and mitigation under high uncertainty and network interdependence. This protocol describes a reproducible digital-twin (DT) workflow that links data ingestion, spatiotemporal risk forecasting, risk propagation simulation, and mitigation policy optimization in a single containerized stack. For prediction, we integrate an interpretable sequence-and-graph model that combines a Bi-LSTM with graph convolution to capture temporal dynamics and network dependencies. For mitigation, proximal policy optimization (PPO) learns actionable decision policies from simulated disruptions, enabling cost-aware responses rather than post hoc diagnosis. To support deployment and replication, we provide a hierarchical key-risk-indicator taxonomy that separates endogenous and exogenous risks, together with version-pinned environments, data contracts, and validation checkpoints. Representative results demonstrate both predictive and decision-level gains. On a 300-node automotive network, the approach improves F1 by 19.3% and extends the early-warning horizon by 5.2 h. In a 2021 U.S. port-congestion replay, PPO reduces delays and emergency-response costs by over 20%. The workflow scales to 500-node simulations with millisecond-level latency while maintaining stable long-horizon performance. Dual validation with historical-event replay and online simulation confirms improved accuracy, robust decision quality, and practical scalability. Limitations and adaptation guidelines are discussed to facilitate transfer to new supply-chain settings.

Introduction

The global supply networks are under constant uncertainty, where local disruptions can propagate across supplier tiers and transport lanes and trigger system-wide slowdowns. In many organizations, risk monitoring still relies on retrospective reports or periodic audits, which are useful for documenting incidents and compliance but are less effective for fast-changing operational streams. When data evolves minute by minute, decision makers need a workflow that supports continuous sense-making, near-term prediction, and timely intervention rather than post hoc diagnosis¹.

Traditional tools such as risk registers and st....

Protocol

We confirm that this study uses de-identified enterprise and logistics records, together with synthetic disruption scenarios, with no human or animal involvement. Access to raw operational logs was granted in writing by the data-owning organizations and approved by the institutional data governance committee prior to data extraction. Data access was managed through role-based access control; all access events were logged; and audit trails will be retained for ≥3 years after publication. When reporting disruption cases, we generalize or mask firm-identifying attributes (e.g., facility names, customer identifiers, contract IDs, and exact geolocations) to reduce re....

Results

Experimental setup

The protocol uses Python 3.11 with PyTorch 2.1, scikit-learn 1.3, NetworkX 3.1, pandas 2.1, numpy 1.27, and optional TensorFlow 2.14. Simulations rely on Graph-SLAM (g2o 2025.10, ROS2), AnyLogic 2025 for agent-based modeling, and ANSYS Fluent 2025 or OpenFOAM v11 for CFD. Data exchange is supported through MQTT v5, OPC-UA, and FastAPI for model serving, with containerization via Docker 24.0 and NVIDIA Toolkit. Required hardware includes ≥16-core CPUs, .......

Discussion

The study’s findings support the view that digital-twin-enabled learning pipelines can strengthen supply chain risk management by coupling state synchronization with data-driven forecasting. The experimental results indicate that the Bi-LSTM–attention backbone, combined with GCN-based spatial encoding, improves predictive accuracy and extends the early-warning horizon, which is consistent with recent evidence that spatiotemporal forecasting benefits from incorporating exogenous signals and network structure i.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AnyLogic Simulation Platform	The AnyLogic Company	N/A	Multi-method modeling software for agent-based, discrete-event, and system dynamics simulations, integrated with TensorFlow
NVIDIA A100 GPU Server Cluster	NVIDIA Corporation	N/A	Used for parallel training of Bi-LSTM-Attention + GCN models and PPO optimization within the digital twin platform