Application of Data Envelopment Analysis to Measure the Technical Efficiency of Oil Refineries: A Case Study
Bibliographic record
Abstract
This paper is an attempt to implement the Data Envelopment Analysis (DEA) approach to measure the relative efficiency of a sample of oil refineries in Iraq over a period of two years, 2009-2010. We demonstrate that DEA is an effective tool for the Ministry of Petroleum (MOP) for monitoring and controlling the performance of oil refineries, which are growing as an important sector in Iraq. The authors followed a case study methodology where data about the inputs and outputs of refineries are gathered and analyzed to compute the relative efficiency of the refineries. Based on the results obtained, 50% of the refineries were efficient in 2009, while 58% of them were efficient in 2010, and the overall efficiency of the refineries studied was about 82% and 87% respectively. Later, inefficient refineries were investigated closely to identify the areas in which the use of resources manifest decreasing returns to scale. We concluded the paper with some recommendations on the applicability of the DEA for oil refinery efficiency evaluation. Due to the absence of research work, in this discipline, in the oil sector in Iraq, this study shall augment our knowledge on how oil refineries in Iraq may apply DEA to measure their efficiency, and how they might use the results to overcome efficiency problems. Although the results of the present paper are limited to the oil refineries studied; the DEA approach could trigger the attention of policy makers in the MOP to apply DEA to improve the efficiency of other DMUs. In addition, other manufacturing and service sectors in Iraq could, also, benefit from 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".