Analysis of the evolution of aerospace manufacturing ecosystems
Bibliographic record
Abstract
The aerospace manufacturing industry is predicted to continue growing. Understanding its evolution is thus essential to prepare optimal conditions to nurture its growth. This research aims to help the growth of emerging aerospace ecosystems by identifying evolution patterns and categorising key enablers that have encouraged the growth of developed ones. The term aerospace ecosystem is used to embrace all the business activities and infrastructure that are related to the entire aerospace’s supply chain in a specific country. Inspired by studies that have successfully combined economics and network science, in this research, bipartite country-product networks are developed based on trade data over 25 years. The United Kingdom (UK), the United States of America, France, Germany, Canada and Brazil’s are first analysed as evidence suggests that their aerospace ecosystems are within the most developed in the world. Then, China and Mexico’s networks are analysed and compared with developed ones, as these countries have evidenced emergent aerospace ecosystems. Results reveal that developed ecosystems tend to become more analogous, as countries lean towards having a revealed comparative advantage (RCA) in the same group of products. Further analysis shows that manufactured products have a stronger correlation to an aerospace ecosystem than primary products; and in particular, the automotive sector shows the highest correlation with positive aerospace sector evolution. Key enablers related to the growth of the UK and Mexico’s aerospace ecosystems are identified and categorised using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) methodologies. Results evidence relevant differences in the categorisation of key enablers among a developed and emergent aerospace ecosystems. On the other hand, it was identified that geopolitical factors and the automotive ecosystem are underpinning enablers for both aerospace ecosystem’s evolution. The final aim is that results of this research could be implemented on emerging aerospace ecosystems by emulating the patterns and key enablers that have characterised the evolution of developed aerospace ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".