The field of system of systems lifecycle management is shifting towards a more network-centric approach, recognizing that traditional linear lifecycle models are no longer sufficient. Key factors such as interoperability, variant and configuration management, traceability, and governance across organizational boundaries are becoming increasingly important. The integration of digital engineering and machine learning components is also gaining traction, with a focus on improving the efficiency and predictability of complex system development and sustainment projects. Noteworthy papers in this area include: The paper on enhancing software product lines with machine learning components, which proposes a structured framework for integrating ML components into software product lines. The study on the return on investment of digital engineering for complex systems development, which provides initial quantitative evidence of DE's potential ROI and its value in improving the efficiency and predictability of complex system sustainment projects.