Comparative Evaluation of Production Efficiency: A DEA Approach
Bibliographic record
Abstract
The rapid changes in oil prices in last five years forced companies to rethink about their manufacturing pro- cess. This paper examines application of DEA and revenue management models for measuring and im- proving of productivity of a Canadian packaging com- pany. The critical success failure factors are also high- lighted. costs, waste and under utilization of equipment. Produc- tivity measurement and improvements are key building blocks of improving operating performances. In last two decades, focus on these have shifted from attempts to characterize performance in terms of a simple indicator) to a multi-dimensional systems perspective. In contem- porary manufacturing settings, managers are required to implement suite of initiatives simultaneously rather than single initiative. In this scenario managers must consider the relative effects of one initiative on the other as each initiative may link to the outcome of the other. This paper discusses the evaluation of productivity measures by employing the method of Data Envelopment Anal- ysis (DEA). Empirical data obtained from a packaging company in Canada used to illustrate the model. The proposed model evaluate relative-to-best performance ef- ficiency of productivity improvement alternatives related to the packaging industry using multiple inputs and out- puts and it also evaluate the relative efficiency of imple- menting multiple alternatives simultaneously.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 | 0.000 |
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 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".