Estimating On-condition Direct Maintenance Cost (DMC)
Bibliographic record
Abstract
Aftermarket support has become a key component of cost and competitiveness in the rotorcraft industry. Both the operator and the rotorcraft manufacturer play a role in aftermarket support. Many rotorcraft original equipment manufacturers (OEMs) are offering fixed price maintenance service programs in their after-market support programs. Since product support may last well over two decades, the desire for low direct operating cost (DOC) and lower life cycle cost (LCC) has become a more visible consideration in the rotorcraft design phase. Direct maintenance cost (DMC), forms a significant part of the DOC and LCC. A subset of DMC, On-condition maintenance cost is a category with unspecified maintenance intervals and presents one of the more challenging estimating efforts, particularly on a new rotorcraft program with no history. The approach used for estimating maintenance costs can strongly influence decision making within the OEM while also educating the customer on better maintenance philosophy and planning. Incorrectly minimizing or excluding the effect of on-condition cost (within the DMC estimate) could have a profound impact on operator and service organizations of the OEM. This paper presents a high-level discussion on the potential refinements that can be made to the Helicopter Association International’s Economic Committee’s Guide for the Presentation of Helicopter Operating Cost Estimates 2010. Estimating the on-condition direct maintenance cost for airframe manufacturers is the focus of the discussion.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".