GenAI-Scenario Digital Twins for ERP-Backed Supply Networks
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P119Keywords:
Digital Twins, Supply Chain Resilience, ERP Systems, Knowledge Graphs, Generative AI, Stress Testing, Scenario Simulation, Policy ModelingAbstract
Global supply networks face increasing vulnerability to pandemics, geopolitical sanctions, cyber threats, and logistical disruptions. Traditional ERP systems offer transaction-level visibility but cannot simulate cascading failures or guide recovery actions. This paper presents a GenAI-Scenario Digital Twin (GSDT) framework that integrates BPMN, Knowledge Graphs, and Digital Twin pipelines for ERP-backed supply chains. The framework continuously ingests ERP event streams and external risk feeds to generate adaptive, policy-aware simulations. Using Generative AI-driven scenario stress tests, it evaluates resilience metrics such as fill rate, backlog days, and time-to-recover under disruptions including port closures, cyberattacks, and sanctions. Validation combines retrospective replay of historical events with prospective stress-testing experiments. The study contributes a reusable simulation toolkit, a resilience metrics catalog, and case study evaluations with industry and agency partners, providing a scalable approach to enhance supply network resilience.
References
[1] Liu, He, Hildebrandt, Buchner, Inzko, Wernert, Weigel, Beyer et al. (2023). A Knowledge Graph Perspective on Supply Chain Resilience. arXiv:2305.08506.
[2] Zhou et al. (2023). HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment. arXiv:2311.04244.
[3] Golan, M. S. et al. (2020). Trends and Applications of Resilience Analytics in Supply Chains. International Journal of Production Research, 58(11).
[4] Performance Indicators for Supply Chain Resilience: Review and Conceptual Framework. (2020). International Journal of Production Research.
[5] Ivanov, D. & Dolgui, A. (2021). A Digital Supply Chain Twin for Managing the Disruption Risks and Resilience in Supply Networks. International Journal of Production Research, 59(7).
[6] Goh, M., Lim, J. & Meng, F. (2022). Modeling Supply Chain Disruptions: From Event Trees to Digital Twins. Computers & Industrial Engineering, 172, 108595.
