# A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization

arXiv: https://arxiv.org/abs/2504.03692 · HTML: https://mlsanity.com/paper/2504.03692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 153 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet](https://mlsanity.com/paper/2411.11451/) (2024)
- [Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2408.07705/) (2024)
- [Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2410.11234/) (2024)
- [Data Considerations in Graph Representation Learning for Supply Chain Networks](https://mlsanity.com/paper/2107.10609/) (2021)
- [Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chains](https://mlsanity.com/paper/2410.18631/) (2024)
- [Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks](https://mlsanity.com/paper/2411.08550/) (2024)
- [Recent Advances in Fully Dynamic Graph Algorithms](https://mlsanity.com/paper/2102.11169/) (2021)
- [Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/2408.05860/) (2024)
- [Graph Neural Modeling of Network Flows](https://mlsanity.com/paper/2209.05208/) (2022)
- [Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning](https://mlsanity.com/paper/2309.14557/) (2023)
- [GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain Networks](https://mlsanity.com/paper/2404.07523/) (2024)
- [Causal Falsification of Digital Twins](https://mlsanity.com/paper/2301.07210/) (2023)
- [A Knowledge Graph Perspective on Supply Chain Resilience](https://mlsanity.com/paper/2305.08506/) (2023)
- [Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approach](https://mlsanity.com/paper/2309.04785/) (2023)
- [Learning production functions for supply chains with graph neural networks](https://mlsanity.com/paper/2407.18772/) (2024)
- [Applying graph neural network to SupplyGraph for supply chain network](https://mlsanity.com/paper/2408.14501/) (2024)
- [Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network](https://mlsanity.com/paper/2206.03469/) (2022)
- [Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting](https://mlsanity.com/paper/2411.06268/) (2024)

## Built on by (most cited first, 2 of 2)

- [Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2511.02957/) (2025)
- [Towards explainable decision support using hybrid neural models for logistic terminal automation](https://mlsanity.com/paper/2509.07577/) (2025)

## Abstract (arXiv)

Supply chain management is growing increasingly complex due to globalization, evolving market demands, and sustainability pressures, yet traditional systems struggle with fragmented data and limited analytical capabilities. Graph-based modeling offers a powerful way to capture the intricate relationships within supply chains, while Digital Twins (DTs) enable real-time monitoring and dynamic simulations. However, current implementations often face challenges related to scalability, data integration, and the lack of sustainability-focused metrics. To address these gaps, we propose a Graph-Based Digital Twin Framework for Supply Chain Optimization, which combines graph modeling with DT architecture to create a dynamic, real-time representation of supply networks. Our framework integrates a Data Integration Layer to harmonize disparate sources, a Graph Construction Module to model complex dependencies, and a Simulation and Analysis Engine for scalable optimization. Importantly, we embed sustainability metrics - such as carbon footprints and resource utilization - into operational dashboards to drive eco-efficiency. By leveraging the synergy between graph-based modeling and DTs, our approach enhances scalability, improves decision-making, and enables organizations to proactively manage disruptions, cut costs, and transition toward greener, more resilient supply chains.