Optimization of overall equipment effectiveness (OEE) factors: Case study of a vegetable oil manufacturing company
Bibliographic record
Abstract
The poor maintenance and usage of the equipment and machines of a vegetable oil manufacturing company adversely affect its competitive advantage. These industries are faced with numerous equipment maintenance challenges in the path to increasing their throughput as well as profitability. To address the said maintenance challenges, process data were obtained for the Overall Equipment Effectiveness (OEE) factors after their Total Productive Maintenance (TPM) implementation in the company. Minitab 21 software was used to analyze the data collected, and the results showed that the mean for quality, availability, and performance obtained were 96.929%, 63.35%, and 61.20%, respectively. This shows that the quality of products is the greatest OEE factor that vegetable oil manufacturing companies must consider meticulously to reduce the six big losses in their production processes. Response Surface Method (RSM) with Central Composite design, with the application of Design Expert 13 software, was used to model, analyze, and optimize the Overall Equipment Effectiveness (OEE) using availability, quality, and performance as the input parameters. The analysis of both the actual and coded values, which is the main contribution of the study, showed that quality has the greatest value followed by availability and performance. It was found that, to effectively reduce the six big losses, the quality, performance, and availability should be targeted as 98.3052%, 81.6022%, and 80.103%, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".