Innovations in supportability engineering using business analysis
Bibliographic record
Abstract
When a Department of National Defence (DND) project manager expresses clarity in how much money he/she needs to implement a project as well as cover through-life costs, it is an optimal situation. The information presented in this paper answers the “CENTRAL” question: what process could be used to integrate Availability Reliability and Maintainability (AR&M) and In-Service Support (ISS) considerations to improve system performance and reduce ownership cost? It is shown that in order to minimize costs and increase the performance of real-time, mission-critical systems it is necessary to influence the requirements and design much earlier than at once thought and before all of the Logistics Support Analysis (LSA) data is available. It is further shown that early consideration of supportability requirements analysis, business case analysis and logistics support cost modelling will not only have a large influence on the requirements for cost-effective procurement of a system but will also have a very large effect on Design for Supportability (DfS). To do this a new Supportability Program Process (SPP) has been created and described which emphasizes the DfS approach and early AR&M considerations. An overview of a process for conducting a Sustainment Business Case Analysis (SBCA) is presented including its relationship to the new SPP. This is rounded out by adapting the DND enterprise standard OmegaPS Analyzer® supportability and modeling tool to record Life Cycle Costs (LCC) and a Baseline Comparison System (BCS). Obtaining Federal Government funding for major acquisitions can be a difficult task as sustainment costs must be estimated up front even before the configuration of the systems to be purchased and fielded is known. Cost validation reports must be secured from the Chief Financial Officer because of the need to exercise fiscal jurisprudence of taxpayers' money. This new process will help ease the task and answer the “CENTRAL” question.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".