Performance Evaluation of Production Lines in a Manufacturing Company Using Data Envelopment Analysis (DEA)
Bibliographic record
Abstract
The concept of producing more output(s) with less input(s) has always been one of the goals of every manufacturing industry.However, continuous evaluation of production systems will ensure that production targets are not only being met but also to ensure that each decision-making unit produces at an optimal level compared to the laid down standard(s).This work evaluated the efficiency of the six most productive production lines in a brewery plant using one of the non-parametric efficiency measurement techniques in data envelopment analysis (DEA).The DEA model for each of the lines was formulated.The relative efficiencies of each of the lines were calculated and the most efficient was chosen as a benchmark.The slacks and surpluses in each production line relative to the benchmark were obtained.The model result revealed that two of the production lines as the most efficient, a reduction in manpower and an increment in product output in some of the lines are required to meet the production benchmark.It may be observed that not all seemingly effective production lines are effective when compared with others within the same system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".