Best Practices in Establishing Business Case for Implementing Blockchain Solution in Aerospace
Bibliographic record
Abstract
The aircraft asset life cycle processes are rapidly being digitalized. Many novel technologies enabled processes of recording these electronic transactions are being emerged. One such technology for recording electronic transactions securely is Blockchain, defined as distributed ledger technologies which includes enterprise blockchain. Blockchain is not widely used in the aerospace industry due to lack of technical understanding and questions about its benefits. Assessment and establishment of business case for implementing blockchain based solution is needed. The aerospace industry is very conservative when it comes to technology adoption and hence it is difficult to change legacy processes. Additionally, the industry is very fragmented. The technology is advancing at a faster rate and applies across geographies under various regulatory oversight which makes blockchain based solution implementation challenging. G-31 electronic transactions for aerospace standards committee of SAE International has conducted a study on determination of cost benefits from implementing a blockchain solution. This study resulted in development of Aerospace Recommended Practice (ARP) 6984 and was published recently. This recommended practice lays out a methodology for qualifying and quantifying the benefits of replacing or augmenting a legacy process with a blockchain solution. This paper presents summary and overview of this study and provides a teaser for ARP 6984.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".