Conceptualization and validation of an intelligent digital twin design framework for supply chain risk management

Abstract

Intelligent digital twins for supply chain risk management have recently gained attention due to rising disruptions, increasing supply chain complexity, and the need for advanced tools. Although various frameworks exist, few clearly identify the necessary data, predictions, and decision-making problems for their development, and even fewer have been validated in real-world case studies. This study fills those gaps by proposing and validating a comprehensive design framework in the automotive sector. The results show that the prototypes developed based on the framework effectively support tasks such as predicting supply chain performance and guiding supplier selection and order allocation while significantly reducing the time needed for risk management tasks.