Implementing MBSE – An Enterprise Approach to an Enterprise Problem
Bibliographic record
Abstract
Abstract Model Based Systems Engineering (MBSE) is now widely accepted throughout the industry, from commercial to aerospace and defense. However, while we understand and accept the principles of MBSE, successful adoption and implementation is still a challenge within the industry. The migration from document‐based systems engineering processes to MBSE requires more than purchasing tools and a one‐week course on Systems Modeling Language (SysML). MBSE does not change the practice of Systems Engineering as defined in the INCOSE SE Handbook or ISO/IEEE 15288, but it does affect the way in which systems engineering processes are implemented and supported within and across organizations. Organizations adopting MBSE must address issues such as new skill and competency requirements for systems engineers, model and data management over the lifecycle of the system, and integration with other engineering tools and processes, among others. It is not a tool problem or a modeler problem. It is an enterprise problem and requires an enterprise approach. The approach must be defined and guided by an enterprise architecture, which is broader than just the engineering tools and their interfaces. It includes the enterprise strategic vision, capabilities, operational concepts, organizations, and material solutions required to achieve MBSE adoption, how they relate to one another, and their evolution over time. This paper provides a broad overview of the fundamentals of MBSE adoption and the broader effort of digital engineering transformation, presenting the digital engineering environment as a system‐of‐systems. It presents the use of enterprise architecture as a roadmap for MBSE adoption within the industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".