How Can I Convince Finance to Fund My Asset Management Program?
Bibliographic record
Abstract
A finance department is primarily composed of budget and accounting areas, with other functions such as investments, debt issuance, rate and fee setting, revenue, billing, and purchasing. Most finance directors come from an accounting background, especially for smaller to mid-sized organizations. Their training is not in quantifying risk, and in fact they are not rewarded for taking risks. However, the principles of life cycle asset management is to manage an asset at its lowest life cycle cost while still meeting a target service level. This directly ties into managing cash flow (current revenues used to pay for operations and maintenance), which in turn impacts various financial metrics such as operating cash on hand and the debt coverage ratio. Separate, but connected, is the capital plan, which can be a combination of both debt and an allocation of reserves. The justification of funding asset management practices involves benchmarking costs and demonstrating how and when assets deteriorate and the maintenance costs increase, the repair costs increase, and if the right investment intervention is not made, the asset could fail prematurely and catastrophically, thus costing a great deal more. This paper walks through the various financial/asset management concepts to convince finance to support asset management and condition assessment activities.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 0.027 |
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".