Collaborative Product–Service Approach to Aviation Maintenance, Repair, and Overhaul. Part II: Numerical Investigations
Bibliographic record
Abstract
This two-part paper proposes a new collaborative approach to airframe maintenance, repair, and overhaul (MRO). A quantitative model is introduced in Part I to represent the business relationships between original equipment manufacturers (OEMs) and MRO enterprises. In Part II, the presented model is used to assess potential financial benefits obtained by each of these stakeholders as a result of the collaboration. The quantitative model is built to capture the main dependencies between an independent MRO operating in South America and its interactions with three major airframe OEMs. Interviews were conducted with MRO and OEM professionals to identify the most impactful operational resources on MRO activities. Stakeholders with different characteristics in terms of production capacity, annual revenue, fleet size, and age are considered in the numerical studies to quantify the viability of the proposed collaborative business model in different scenarios. The obtained results show that optimal investment levels must be determined for each stakeholder to ensure the viability of the proposed collaborative business model, confirming the need for a quantitative method to aid service designers making decisions. This collaborative model contributes to the relatively scarce literature on the topic and promotes effective and structured collaboration between OEMs and MRO enterprises aiming at delivering higher added value to customers (operators).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".