Improving the Energy Operators Efficiency by Business Process Reengineering in Jordan
Bibliographic record
Abstract
In the early 1990’s, the concept of “Business Process Reengineering” (BPR) was first introduced by Michael Hammer and James Champy. Nowadays, many projects aiming a better management focus on better processes, as obtained by reengineering the existing ones, using different techniques. This paper approaches this aspect of the management processes redesign in a field which has been (and still is) highly regulated and ruled by bureaucratic laws: electricity production and distribution. Introducing the corporate management in this field is not always possible but, up to some extent, it is desirable, both because the competition occurred and is growing but also due to the globalization of the energy market. Processes control is necessary for almost all processes run at the level of the energy operators, concerning production, distribution and trading. This paper identifies the most important aspects to be taken into account in order to implement corporate management redesign and especially BPR in the energy sector of Jordan, emphasizing the resemblances and differences between the two cases and offering recommendations for fulfillment of this approach.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".