Pattern-based decision-support-tool to enhance resilience and sustainability in production networks: A framework proposal and application

• Decision support tool enhances sustainability and resilience in production networks. • Fosters data-driven decision-making using simulation and a knowledge-based system. • Pattern-based methodology enables structured reuse of expert knowledge. • Supports cross-organizational learning and informed decision-making. • Validated in real-world electronics industry use case. Global production networks have become increasingly complex and interdependent due to decades of intensified globalization. While these developments have driven efficiency gains and competitive advantages, they have also led to significant environmental impacts. In response, regulations such as the Corporate Sustainability Due Diligence Directive are compelling companies to improve sustainability across their value chains. At the same time, recent local and global disruptions have underscored the critical importance of resilience. As a result, organizations must systematically evaluate and improve the sustainability and resilience of both their own operations and the broader production networks. However, research shows that lessons from past crises are often not fully internalized, and companies continue to struggle with fragmented domain-specific knowledge. Moreover, data alone does not yield actionable business insights. It only generates value when effectively combined with analytics and expert interpretation. Many of the proposed solutions for improving sustainability and resilience are not entirely new, but they require more integrated and systematic application to be effective. Against this background, we propose a decision support tool grounded on an iterative, pattern-based methodology. It combines simulation-based analysis using AnyLogistix with a knowledge-based database to enhance resilience and sustainability in production networks. The pattern-based approach enables the systematic identification, capture, and reuse of proven strategies. The methodology is successfully validated through a real-world use case in the electronics industry and shows that both resilience and sustainability can be effectively enhanced through the proposed approach.