Operations Excellence Management System (OEMS)
Bibliographic record
Abstract
Written for users, this book provides a structured approach with processes for implementing OEMS based on the learnings and experiences from companies who have implemented OEMS. The book leverages the knowledge of experienced OEMS personnel to provide a compelling sense of direction for organizations in the implementation of OEMS. The book includes sample templates and tools where necessary to ensure successful implementation and sustainment. The content of this book provides a testing methodology for implementing an OEMS across any organization while avoiding the pitfalls others have encountered along the way. The book: Provides a simple and easy process to follow for implementing an OEMS Offers organizations an opportunity to avoid the implementation errors of early adopters and provides them with the ability of learning from the experiences of others Equipped with tools and processes to make implementation and sustainment very effective, thereby avoiding false starts Designed to improve HSE, business reliability, efficiency, effectiveness, and performance on an ongoing basis Presents a simple pathway for helping organizations across all industries including those that operate within the various segments of the Oil and Gas business, to become more operationally disciplined in the way we do business and operate our assets in a high-risk operating environment
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".