6.2.3 Modeling ISO/IEC 15288 & Tailoring Enterprise Systems Engineering Processes for an Organization's Success
Bibliographic record
Abstract
Abstract Tailored and integrated organizational processes are fundamental drivers for organizations to achieve efficiency and success. The organization's processes represent the means to achieve the organization's business objectives in terms of communications, functionality and systems related products. Developing the organization's processes architecture model must integrate both managerial and technical systems development efforts to produce efficient tasks and products. This document addresses the complexity of capturing the ISO/IEC 15288 “Systems engineering‐System life cycle processes” systems management and engineering processes and their outcomes in an integrated architecture model. Several significant issues and concerns are identified that relate both to the standard's contents and to tailoring the standard to an organization's processes. Assumptions for tailoring the model are identified, in addition to topics that developers of integrated and organizational processes need to consider prior to development of their integrated and tailored processes. The four topics addressed are: Systems Engineering Standards and ISO/IEC 15288's unique perspective The importance of modeling a standard and an organization's processes The modeling of ISO/IEC 15288 including tailored perspectives Modeling an organization's integrated processes to meet a customer's objectives
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".