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

• URL: http://arxiv.org/abs/2504.03692v1
• Date: Sun, 23 Mar 2025 19:27:58 GMT
• Title: A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization
• Authors: Azmine Toushik Wasi, Mahfuz Ahmed Anik, Abdur Rahman, Md. Iqramul Hoque, MD Shafikul Islam, Md Manjurul Ahsan,
• Abstract summary: Supply chain management is growing increasingly complex due to globalization, evolving market demands, and sustainability pressures.<n>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.<n>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.
• Score: 0.37109226820205005
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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