Investigating the technical and scale efficiency of cement companies in Saudi Arabia
Bibliographic record
Abstract
Cement & material sector is instrumental in infrastructural development of any economy. The same holds about Saudi cement sector which has contributed substantially to the economic and construc- tion boom in Kingdom of Saudi Arabia (KSA). The cement sector of KSA holds the highest place among other GCC countries. Though, during the last couple of years, the sector seems to be grap- pled with capacity and weighted down due to certain reasons. Nevertheless, almost all of the cement companies in KSA are underplaying their actual capacities. Still, plenty of untapped growth oppor- tunities for cement sector are available in KSA and other GCC countries. Henceforth, considering the growth potential and taking cue from the current scenario of KSA cement sector, the current study endeavors to measure the efficiency of listed cement companies in KSA. The study endeavors to be engrossed in identifying a set of companies which plays on efficiency frontier. Therefore, the technical efficiency performance of fourteen listed cement companies in KSA was measured using Data Envelopment Analysis (DEA) methodology. Two basic models of DEA methodology (i.e. CRS & VRS) were used to estimate the pure and technical efficiency of identified DMUs over a period of four years from 2016 to 2019. The study reveals that over a period of four years and on an average efficiency scale, only 23% of the firms were purely technically efficient on a CRS scale, while 46% of the firms were technically efficient. Only 23% of the firms were scale and technically efficient. Though, companies in the sector have a vast potential to outperform on efficiency front. Yet, the overall efficiency level among Saudi cement companies are remained depressing. Moreo- ver, the study has noticed that the companies which are inefficient did not have a considerable distance from the efficiency frontier. The study also provided significant insights on the input fac- tors causing inefficiency and suggestion to achieve the total technical efficiency. Furthermore, the efficiency analysis also provided benchmarking firms, which are efficient under several criteria for others to imitate their best practices for becoming a significant player on efficiency frontier.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".