This paper summarizes research on the organizational adoption of Digital Engineering (DE) and Model-Based Systems Engineering (MBSE). For the last five years, the Systems Engineering Research Center (SERC) has conducted a sustained series of research tasks evaluating and developing a model for DE/MBSE adoption. This paper summarizes the results for each stage of this research, presents the derived model of enterprise adoption factors, then outlines an adoption strategy using lessons learned and the 12 highest impact adoption factors. An extensive list of references is included. Organizational adoption of DE/MBSE requires a strong foundation in systems engineering and a multi-year organizational digital transformation strategy. There are two overarching measurable outcomes that should guide this transformation. The first is the increased levels of knowledge gained from integrating a formal systems level model to lower-level component and disciplinary models, resulting in improved system quality and lower total effort. The second is increased efficiency from digital integration of data and models, resulting in reduced cycle times. To scale adoption of DE/MBSE methods, processes, tools, and human capital, the organizations must be able to visualize these outcomes as they grow over time. The body of research summarized here provides a justification and initiating framework for organizations wanting to undergo digital transformation of their engineering practice.
A key part of digital transformation is the application of digital engineering and model-based systems engineering (MBSE) tools for mission engineering. However, current digital engineering practices and standards lack maturity and current guides are often too broad for the intention of reusability and a wide range of application. Additionally, the diversity of system-of-system constituents deployed for missions, and their complex system behavior and stakeholder interactions, pose complex challenges for its successful application. We propose a general framework that builds on existing guidelines to better organize and facilitate the digital mission engineering process. The methodology is categorized into multiple abstraction levels to demonstrate the existing layers of information produced and identify relevant stakeholder viewpoints of data to improve communication and collaboration. A pilot implementation demonstrates the applicability of the framework to construct a cislunar reusable mission, and the benefits were determined based on digital engineering reference qualitative metrics, observations, and perceptions. The benefits of the digital-enabled framework include increased traceability and reusability, enhanced support for users, and improved communication and knowledge. Additional work remains to develop the early works of the proposed framework and address pain points from academia and industry application.
A growing, increasingly urban population with a high ecological footprint is raising the threat of global food insecurity. While sustainable food systems such as mobile plant cultivation units (MPCUs) combined with new digital technologies such as digital twins provide a promising mitigation strategy, cities often differ significantly in available resources, as well as climate and infrastructure, making a one-size-fits-all design solution insufficient. A set of digital twin use case scenarios and reference architectures for understanding how to design, manufacture, service, and retire MPCUs in different city contexts provides a first step toward using data-driven digital twins to help address these challenges and promote sustainable cities. This paper contributes to this effort by advancing a use case scenario where a digital twin is used to automatically grow selected plants in MPCUs by monitoring relevant physical conditions; based on this scenario, a first-level functional architecture for the digital twin is proposed. The proposed architecture shows promise in light of existing reference architectures for digital twins created for a different domain. Research is continuing on how to design and operate a digital twin in conjunction with a MPCU.
The US Aerospace and Defense industries are in the midst of a transformation to a digital paradigm. As an example, the US Department of Defense (USDOD) released a strategy for implementation of digital engineering (DE) in 2018. The strategy defined key focus areas of DE, one of which was focused on culture and workforce development. To meet the need of development of the USDOD workforce, we have created the Simulation Training Environment for Digital Engineering (STEDE). A subset of STEDE is focused on digital acquisition artifacts, which are expected contribute to acceleration of the acquisition process. Rather than acquisition artifacts existing as static, largely textual documents; the acquisition artifacts are digitally transformed to become a dynamic set of data and models in a digital ecosystem, interconnected through a digital continuum. The Systems Engineering Research Center (SERC), to which our research group belongs, has a number of projects that advance knowledge of digital acquisition artifacts, such as the digital test and evaluation master plan (d-TEMP), which is expected to complement the pursuit of educational research presented in this article. However, we have specifically selected to focus this article on the digital systems engineering plan (d-SEP). While we do not share the curriculum in this article, we use this article to provide insights that we have uncovered during our pursuit of the basis for DE education.