Session: CIE-05-01CIE Graduate Student Poster Symposium

Paper Number: 74650

Start Time: August 18, 10:00 AM

74650 - Developing a Digital Twin Framework for Improving Resilience in Military Supply Chain (Msc) of Defense Industries 

The focus in this research is on employing a model-based and data driven approach to develop a Digital Twin (DT) framework for improving the resilience of Military Supply Chain (MSC) of defense industries. The model-based approach will be used to model and simulate the current MSC in order to determine its current resilience and peformance. While  Machine Learning (ML) as a data driven approach will used in developing a prediction model using supervised learning algorithm (linear regression). ML will be adopted in this research due to its consistency and transparency over other approaches and its flexibility to train and test the models. The DT will enable managers or military logistic planners to effectively manage disruptions by providing a near real time analysis of the MSC. Also, it will allow to create a continuous cycle of improvement and adjustment of the entire supply chain. However, to the best of my knowledge, no research has applied both DT and ML in improving the resilience of MSC which makes the research unique and interesting. Therefore, the aim of this research is to develop a DT framework for improving the resilience of MSC of Defence industries. To test and validate the models developed, a scenario-based model of the Military Ammunition Supply Chain will be used in this research.

Presenting Author: shehu sani University of Liverpool

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