Prioritization of Best Practices in the Implementation of Model‐Based Systems Engineering
Bibliographic record
Abstract
Abstract Model‐based systems engineering (MBSE) is a form of systems engineering that is reported to lead to many benefits; yet few companies have successfully implemented MBSE, partly due to a predominance of guidance for adoption that does not recognize differences in organizational context. This paper uses a combination of a literature review and semi‐structured interviews to evaluate the hypothesis that the applicability of best practices varies on a case‐by‐case basis due to factors such as company size, industry, and location. A list of best practices is compiled from existing works and validated with interviews, ultimately resulting in a more complete and accurate list of best practices, along with a recommendation that each company should uniquely assess their circumstances and prioritize this list of best practices accordingly. The findings presented in this paper provide improved guidance towards the use of best practices for successful MBSE implementation.
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 | 0.165 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".