Implementation of Lean Manufacturing to Increase Aircraft Availability and Productivity: a case study
Bibliographic record
Abstract
Aircraft availability is a crucial factor in ensuring the continuity and effectiveness of pilot training programs.In the aeronautical industry, maintenance complexities and operational variables represent challenges to maintaining a constant operational fleet, resulting in economic losses and operational delays.This study aims to address the problem of low aircraft availability in a civil aviation training center by implementing Lean Manufacturing tools to increase aircraft availability.The research demonstrates that by applying techniques such as Total Productive Maintenance (TPM), process automation, and predictive maintenance, it is possible to increase aircraft availability and reduce operating costs.A simulation model was developed using Arena software to validate the impact of these improvements, showing an increase in availability from 60.56% to 83.75%.The results highlight a significant reduction in maintenance downtime, increasing availability by 12% due to a decrease in MTTR from 7.278 Hrs/failure to 3.807 Hrs/Failure and an increase in MTBF from 12.7 Hrs. to 60.5 Hrs., as well as improvements in operational efficiency with an 86.67% improvement in filling out the Aircraft Technical Log (ATL) and a 73.62% improvement in filing flight plans, which contributes to increasing profitability from PEN 29,100 to PEN 52,700.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".