*Result*: Building resilient closed-loop supply chains: resilience enablers and diversified sourcing under global disruptions.

*Building resilience against global disruptions is vital for modern supply chains. This research explores under-researched aspects of closed-loop supply chain (CLSC) resilience, focusing on network structure and resilience enablers. It examines the multi-directional ripple effect in CLSCs, where disruptions spread forward (production/distribution), laterally (same-tier facilities), and backward (reverse logistics), analysing them as complex, interconnected systems, unlike prior studies on simpler networks. We simulate real-world disruption/recovery scenarios to evaluate the resilience of various CLSC structures against global disruptions. This analysis highlights how the combination of proactive planning, reactive response, and agility can enhance resilience, demonstrating their synergistic impact. The study also investigates the impact of supplier diversification on resilience, showing that while diversification improves performance under localised disruptions, its effectiveness diminishes when disruptions become global. Based on the findings, we propose a bi-objective optimisation model that balances resilience gains with cost constraints, focusing on supplier diversification for optimal CLSC performance. A case study in Australia validates the model, demonstrating resilience improvements of 20% with $15,000, 27.1% with $30,000, and 32.9% with $50,000 relative to the baseline. This research provides decision-makers with flexible strategies, integrating a proactive-reactive approach, strategic and real-time agility, and supplier diversification to enhance CLSC resilience while considering budget. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)*

*Full text is not displayed to guests* *Login for full access*