Availability optimization using spares modeling and the six sigma process
Bibliographic record
Abstract
This paper discusses applying reliability modeling as an integral part of the six sigma improvement process for the purpose of balancing long term cost of ownership and productivity improvement within the petro-chemical industry. This is a practical yet scientific approach which has reduced the risk (production losses) caused by inadequate spare equipment stocking strategies while also considerably reducing the overall spare equipment stocking level at many of the company's facilities. The Six Sigma process also has been applied not only to "fix it right but fix it right once" to sustain the gains through the use of the MAIC (Measure, Analyze, Improve and Control) process. The paper does not discuss in detail the complexities of building and applying the simulation models which are developed to support reliability and storage optimization decisions. It does address how the simulation process is tied with the six sigma process in providing the efficiencies discussed. It is the goal of this process to have zero productivity losses with the lowest spare equipment inventories. This is the essence of the spare equipment risk equation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".