Estimating and using direct operating cost as a design parameter
Bibliographic record
Abstract
One of the many challenges faced by Reliability and Maintainability (R&M) engineers is convincing design teams and management of the impact of component or system failures and the resulting maintenance. This is especially true when multiple design options are under consideration. Even when all options will, more or less, achieve the desired operational result, there are almost always compromises that need to be made to select the single most optimum design for production. Almost universally, some of the compromises that will be made involve Reliability, Maintainability, and Cost. One option may be projected to achieve very attractive R& M metrics, but is extremely expensive to procure or to produce. Another option may prove to be inexpensive, but has R&M characteristics that are degraded, perhaps significantly. R&M Engineers will undoubtedly champion the first option. However, typical R&M metrics, such as Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR), can be a tough sell. Cost, whose impact to a design program is immediate and inherently well understood, tends to be judged more critically than MTBF or MTTR, whose real world im pact can be somewhat difficult to comprehend. This paper explores an alternative design metric that combines Reliability, Maintainability, and Cost into a single, less ambiguous, and customer centric metric: Direct Operating Cost (DOC). DOC represents how much a customer can expect to spend keeping a system operational over a defined period of time. By using DOC in the design process, a design program can look beyond customer acquisition cost to focus on creating solutions that provide the best overall value to their customers.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".