The early design phase of complex, capital-intensive systems is critical for shaping their architecture and value proposition. However, such systems face numerous challenges from technological, economic, market, and regulatory domains. In addition, considering system-of-systems introduces new hurdles as the focus shifts from measuring performance to assessing overall effectiveness. Together with the growing trend of servitization, where traditional products are combined with value-added services to deliver functions, a lot of uncertainty is introduced during design decision-making. To handle these uncertainties, systems engineering literature advocates for incorporating lifecycle properties into the system that enable the system to deal with these uncertainties once deployed. Systems that consistently meet evolving stakeholder expectations, despite the changing contexts, are called value-robust systems. Changeability is one such property that allows the system to achieve value robustness by changing internally in response to changes externally. During the design stages, the goal is to identify and integrate options that would enable the system to exercise change and sustain value under all conditions.

In this light, this thesis aims to support the integration of changeability in complex systems by facilitating its assessment during the early design stages. To achieve this goal, it first identifies the existing methods and challenges in changeability assessment for achieving value robustness. To address these challenges, it proposes the Changeability Assessment in Systems during Early Design (CASED) method, which supports development teams in creating value-robust systems in the face of uncertainty. CASED is one of the core contributions of this work, allowing a holistic consideration of identification, quantification, and valuation of changeability during early design stages. It maps the expected mean value and expected standard deviation for each design as a function of changeability level, which serves as a guide for decisions concerning changeability. Additionally, this thesis explores the use of Extended Reality technologies to address perceptual complexity by visualizing operational scenarios and proposes designing for changeability as a mechanism for creating value-robust circular systems.

Today, increased global competition and regulatory demands push organizations to find innovative ways to design and create value. One way is to examine Product-Service System offerings, which enable more stable revenues and foster tighter collaboration and value co-creation between the provider and customer. However, this often means adopting a System-of-Systems (SoS) approach where the value creation is dependent on successful collaboration between constituent systems and actors. SoS also leads to higher complexity and uncertainty, making them more challenging to design, especially in the early stages where knowledge is limited. Therefore, this thesis investigates how to leverage simulations and operational data to explore the value creation in SoS design.

Methodologically, the work adopts a Design Research Methodology and Participatory Action Research approach, combining systematic literature reviews with six industrial case studies in the construction machinery and climate resilience sectors. The case studies originate from a series of research projects conducted in collaboration with various industrial and public partners between 2020 and 2025. 

The research introduces two main contributions. First, the Value–Data Framework, which operationalizes Value-Driven Design by linking value modeling to operational datasets, enabling transparent and quantitative assessment of value creation. The value model introduces a two-step value hierarchy to enhance transparency and address the growing complexity of SoS. Operational data is defined and classified clearly to show where and how it can be leveraged. The second contribution, an Integrated Simulation Platform, equips design teams with the ability to develop effective simulations for SoS modeling in early design stages. The platform explicitly addresses multi-fidelity to guide developers in balancing the computational complexity and accuracy. Finally, both contributions are demonstrated in quarry case studies to validate and highlight their usefulness. Lastly, the SoS simulations are contextualized for Digital Twinning, and the use cases and needs for deploying Digital Twins in climate resilience efforts are examined.

The findings advance theory by bridging Value-Driven Design and SoS simulations, and provide practical guidelines for managing fidelity trade-offs and data–value integration. Industrial implications include improved decision support for electrification, autonomy, and resilience strategies.