Blockchain for Aircraft Part Traceability in MRO (Maintenance, Repair, Overhaul)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it