Process Mapping for Interdisciplinary Aerospace Processes: A Case Study
Bibliographic record
Abstract
The multidisciplinary nature of aerospace processes demands coordination across diverse functions, each contributing to the design, development, sustainment, and compliance of highly regulated aerospace products. These processes involve various interconnected and interdependent elements that influence execution, making it challenging to fully deconstruct and reveal the underlying complexities and interactions that shape the overall process. This thesis explores a practical adaptation of a process mapping technique within the aerospace industry, focusing on a single case study at a Canadian aircraft maintenance company. Through this case study, the thesis illustrates how multiple process mapping approaches can offer different perspectives and enhance process transparency. A current-state manufacturing process is mapped at five different levels of detail: Level 1 provides a high-level overview of the process with milestones and principal tasks; Level 2 incorporates information flow through artifact types; Level 3 adds the roles and expertise required for each activity in the process; Level 4 introduces communication activities; and Level 5 details the working time for each activity. By analyzing the results from each mapping level, the study evaluates the usability and benefits of incorporating different process elements. The findings show that process mapping is not only suitable for visualizing task-specific workflow but can be customized to meet other end-user needs. It was found that no single level of detail is entirely self-sufficient, and that combining elements such as information flow, roles, communication and time provides distinct perspectives offering value across a wide range of use cases and 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".