A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization
Azmine Toushik Wasi, Mahfuz Ahmed Anik, Abdur Rahman, Md. Iqramul Hoque, MD Shafikul Islam, Md Manjurul Ahsan
TL;DR
The paper proposes a Graph-Based Digital Twin (GDT) framework to address the fragmentation, scalability, and sustainability gaps in modern supply chain management. By integrating a Data Integration Layer, a Graph Construction Module, and a Simulation and Analysis Engine within a graph-based DT architecture, the approach enables real-time modeling, disruption forecasting, and prescriptive optimization. Key contributions include embedding eco-efficiency metrics into dashboards, supporting static/dynamic/multi-layer graphs, and leveraging Graph Neural Networks (GNNs) and reinforcement learning for proactive decision-making. The framework aims to improve resilience, reduce costs, and promote greener logistics through continuous data-driven insights and an interactive visualization interface. The work highlights practical implications for industry adoption, potential interdisciplinary applications, and avenues for future research on scalability, interoperability, and ethical considerations.
Abstract
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.