Multidisciplinary design analysis and optimization plays a vital role in the transformation of design processes in aeronautics, ground/water transport or the energy sector. Large design spaces and/or high-resolution numerical models call for gradient-based optimization and adjoint methods. Uncertainty handling is crucial in the context of robust design. Algorithmic and parallel/HPC efficiency, interoperability/extensibility and usability of the software solutions are key scalability aspects for large coupled high/mixed-fidelity problems – e.g. CFD-CSM simulations. Multi-fidelity methods, efficient surrogate models or machine-learning/data-driven techniques can accelerate the design evaluation. This minisymposium aims to review the state of the art in integrating the disciplinary advances into large scale, complex, and/or coupled industrial scenarios. Particularly, contributions on the following topics are invited: • emerging frameworks, packages and libraries for large-scale MDAO • scalable and robust solution and optimization algorithms • gradient/adjoint-enabled solutions • geometry modelling for MDAO • data-driven techniques • MDAO for transient/dynamic and periodic systems • industrial MDAO applications, benchmark and verification studies