2.6.1 Extending the Systems Engineering Methodology to Include Supportability Engineering
Bibliographic record
Abstract
Abstract Systems' engineering has always been an essential part of developing integrated solutions. From its' earliest inceptions, systems engineering dealt with providing a solution that balanced performance and operational requirements at the lowest life cycle cost. As an art form, systems' engineering was based upon the methods and processes of individuals. As the tools, methodologies and philosophies of systems engineering evolved, it was transformed from an art to a science. This transformation is demonstrated in evolution that occurred through IEEE 1220, EIA 632, and EIA 731. While many of the attributes of these guidance documents map in to understood design areas such as hardware, software, and ease of manufacture, they do not clearly map into areas such as support strategy, impact to total ownership cost, maintenance planning and technology refresh cycles. These later actions fall under the discipline of supportability engineering. The systems engineering model contains numerous opportunities for supportability linkage, however the supportability hand‐offs were undefined when the systems engineering standards were released. This is because systems engineering guidance documents were being written at the same time supportability engineering was evolving to a standalone entity. The result is the documents have a strong interface, but without the necessary details to effectively integrate the disciplines. This paper describes the interconnections and key linkages that need to be addressed to flow information between supportability engineering and systems engineering, and the further evolution of the systems engineering process through assimilation of supportability engineering.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".