On the Integration of Additive Manufacturing for Aircraft Spare Parts Inventory Control
Bibliographic record
Abstract
Abstract Spare parts inventory management represents a challenge for aircraft companies. Determining the optimal allocation and consumption of spare parts is problematic due to the intermittent demand. Original equipment manufacturer (OEM) uses different models to evaluate inventory stock level to avoid the non-availability of the desired spare parts when required. With the recent implementation of additive manufacturing (AM) in many sectors, the implications of AM for spare parts inventory management and control models need more attention. This paper aims to evaluate the advantage of AM integration for spare parts optimization in a multi-echelon inventory system. It compares three scenarios for non-moving, slow-moving, and fast-moving spare parts. A scenario-based modeling approach is followed to draw out insights for managers. The first scenario considers the conventional case where there is no integration of AM. The second scenario considers AM integration only in the central maintenance center (CMC). The third scenario assumes AM integration in CMC and regional maintenance centers (RMC). This analysis showed that when AM repair time is inferior to conventional process (CP) repair time, the best scenario for AM manufacturing integration is a decentralized AM location. And when AM repair time equals CP repair time, and AM repair probability is superior to 70%, the decentralized scenario still the optimal integration solution. However, when the AM repair time equals CP repair time, and the AM repair probability is inferior to 70%, the centralized scenario is the optimal integration solution. Moreover, non-moving and slow-moving spare parts are the most suitable categories for optimal AM allocation. Finally, the paper offers guidelines on adopting AM in the aircraft supply chain and the impact on spare part inventory management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".