A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908302557839360 |
|---|---|
| author | Wasi, Azmine Toushik Anik, Mahfuz Ahmed Rahman, Abdur Hoque, Md. Iqramul Islam, MD Shafikul Ahsan, Md Manjurul |
| author_facet | Wasi, Azmine Toushik Anik, Mahfuz Ahmed Rahman, Abdur Hoque, Md. Iqramul Islam, MD Shafikul Ahsan, Md Manjurul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03692 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization Wasi, Azmine Toushik Anik, Mahfuz Ahmed Rahman, Abdur Hoque, Md. Iqramul Islam, MD Shafikul Ahsan, Md Manjurul Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2504.03692 |