A bi-level supply chain resilience model using cloud manufacturing

Abstract

Globalization has heightened supply chain vulnerability to disruptions such as pandemics and natural disasters. Emerging digital transformation technologies, including digital supply chain and cloud manufacturing, offer a promising approach to mitigate disruptions and improve supply chain resilience by connecting manufacturers through shared information; however, it is often hindered by data security and privacy concerns. This study introduces a bi-level supply chain resilience model incorporating cloud manufacturing and a three-tier data privacy classification to balance efficiency, resilience, and privacy preservation. At the network level, share-aggregated, safe-to-share data optimizes task assignment; at the node level, suppliers locally schedule operations based on confidential data. Through case studies leveraging NSGA-II and Mixed-Integer Programming (MIP) for optimization, the model demonstrates a trade-off between resilience and operational efficiency. Results show that the bi-level approach enables dynamic supply chain adaptation while protecting sensitive supplier data, reducing lead times and transportation costs while maintaining supply chain resilience. These findings highlight the potential of cloud manufacturing as a scalable and privacy-preserving solution for enhancing supply chain resilience.