Transitioning to MBSE in a Large Systems Engineering Organization that Develops Complex Mission System for Helicopters
Bibliographic record
Abstract
Transitioning from a Document Based Systems Engineering approach to a Model Based Systems Development (MBSD) approach in a large systems engineering organization with both legacy and start up programs presents a serious challenge. MBSD differs from Model Based Systems Engineering (MBSE) in that it applies across the design disciplines (SW, I&T, HW, CM). Barriers include concerns about increased cost and schedule to new programs and minimizing impacts to legacy programs with a large investment in non-model based artifacts. In spite of these concerns, future programs will demand a systems engineering environment that enhances the ability of the engineering team to collaborate across both disciples and geography. For this reason Lockheed Martin's Mission Systems and Training (MST) facility in Owego NY, whose primary product includes complex mission systems for manned and unmanned helicopter systems, has begun the transition from a Document Based Approach to a Model Based Approach. Several key goals associated with the transition include providing a turnkey approach to programs, automated generation of systems engineering work products from the model and support for reusable software components. This paper discusses the approach to defining and achieving these goals and provides recommendations for organizations that are interested in transitioning to MBSD.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".