This thesis explored what factors can aid in determining the suitability for organizations to make the switch from Systems Engineering (SE) to Model-Based Systems Engineering (MBSE). While MBSE offers many benefits, such as better traceability, improved collaboration between disciplines, and fewer late-stage design errors, many companies still struggle with deciding whether the switch is the right move for them. Although several tools exist to help assess how far an organization is in its MBSE journey, there is little support available for making the initial decision of whether MBSE is suitable in the first place. This research aimed to fill that gap by identifying key factors that influence the suitability of MBSE adoption within organizations and by developing a factor-based assessment tool.

The research focused on five main questions: (1) What factors determine when MBSE implementation is recommended? (2) How do these factors interrelate? (3) Which factors are most critical in the decision-making process? (4) What are the expected benefits and drawbacks of MBSE compared to SE? (5) What types of models and roles are involved in MBSE? Due to limited time being available for this thesis, the emphasis of the research was mainly on the first and third questions.

The research started with a literature review to explore the definitions of SE and MBSE. This was followed by a brainstorm to explore which factors could be relevant and provide a focus for the further literature review. Then the factors found in the brainstorm and literature review were compared to one another and the factors were refined and categorized to be used as a factor list as input for expert interviews. A preliminary factor list was created based on both theoretical knowledge and practical expectations. These included project characteristics, organizational capabilities, and cost considerations among other factors. The factors were then grouped based on the phase of adoption, distinguishing between those relevant before implementation, during implementation, and those influencing long-term sustainability.

In the second phase, interviews were conducted with MBSE experts from The Collective, a consultancy firm with experience in implementing MBSE in various organizations. Interviewees were asked to come up with factors themselves and assess the importance thereof. Later they were asked to reflect on the predefined list, decide whether or not they believed the factor was relevant and give their opinion about the importance of the factor. The participants clustered the factors and provided their view on the order in which they should be addressed.
The interviews followed a structured format and were designed to allow both open discussion and targeted evaluation of the factor list. Interviewees had experience in various sectors, including infrastructure and water management, and represented different roles, such as systems engineers and project managers. Their feedback provided valuable insights into practical experiences, challenges faced during implementation, and lessons learned from both successful and lesser successful MBSE transitions.

The interviews confirmed most of the factors that were identified during the literature review phase, and added new ones. Four factors stood out as most relevant to look at first when deciding whether or not to adopt MBSE: project complexity, whether the system is new and needs to be designed or it being an existing system combined with the amount of (expected) documentation, the organization's maturity with SE, and the risk associated with making design mistakes.
These four factors can serve as boundary conditions. If they are not addressed or present, the potential success of MBSE implementation is likely to be limited.

This thesis concludes with discussing limitations of the research and potential biases, combined with recommendations for future research. Some limitations include the relatively small number of interview participants, the focus on a single consultancy firm, and the qualitative nature of the study. Future research could validate the factor list through quantitative surveys or case studies in other sectors.