Blockchain for Aircraft Part Traceability in MRO (Maintenance, Repair, Overhaul)
Bibliographic record
Abstract
Aim: The study aims to examine the application of blockchain technology as a secure, reliable, and tamper-resistant solution for lifecycle record management of aircraft parts within the aviation Maintenance, Repair, and Overhaul (MRO) sector. Methods: The study adopts a design-oriented and case-based approach to evaluate the integration of blockchain technology with existing MRO systems. A permissioned blockchain architecture is proposed, leveraging smart contracts for automated compliance verification and maintenance scheduling. Real-time aircraft telemetry is ingested using Apache Kafka and processed through Apache Spark to validate and enrich data before being recorded on the blockchain. A proof of concept and case study are used to assess system performance, auditability, and integration challenges with enterprise resource planning (ERP) systems. Results: The findings demonstrate that blockchain implementation significantly improves auditability, data accuracy, and time efficiency in aircraft parts traceability among original equipment manufacturers (OEMs), MRO providers, and aviation authorities. The proof of concept highlights reduced risks of record tampering, improved regulatory compliance, and enhanced transparency across the aircraft parts lifecycle. However, challenges related to system integration, implementation costs, scalability, and market adoption barriers are also identified. Conclusion: The study concludes that blockchain technology has strong potential to reshape trust, transparency, and productivity in aircraft parts record-keeping within the MRO environment. By providing a secure digital footprint for serialized parts, blockchain serves as a foundational technology for advancing the digital transformation of the aviation MRO ecosystem. Recommendations: The study recommends adopting a phased, three-stage blockchain implementation strategy supported by regulatory alignment and cross-stakeholder collaboration among OEMs, MRO organizations, and aviation authorities. Future efforts should focus on cost optimization, ERP integration frameworks, scalability testing, and industry-wide standards to enable sustainable and widespread adoption of blockchain-based MRO solutions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".