Strategic Decision Making Model in Maintenance Management: Is it right time to learn from failures?
Bibliographic record
Abstract
23rd of July 1984 Illinois Union oil refinery explosion, 5th of May 1988 Louisiana Shell Refinery explosion, 6th of July 1988 Piper Alpha disaster, 23rd of March 2005 Texas City Refinery explosion, 20th of April 2010 Deepwater Horizon oil spill in the gulf of Mexico and finally 6th of July 2013 Lac-Megantic Quebec Canada. Where is the problem coming from? Is it multi criteria decision making problem? How do we need to optimised maintenance activities and cost? Is it safe to consider Run to Failure (RTF) strategy for any equipment? Is it cost effective to implement Condition Based Monitoring (CBM) for any equipment? Is it right time to look at the strategic from more strategic level than operational level? This study identified factors that influence policies and practices of maintenance management to improve asset management from more strategic level than operational level. Criteria identification process was carried out through an extensive literature review and roundtable discussion with experts. The correlations of these criteria and the relative importance of one criterion with respect to the others were identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".