Downtime Analysis of a Mayo Bottling Line During the Ramp-Up Period: A Case Study
Bibliographic record
Abstract
Manufacturing companies face many challenges when trying to meet the market and their clients’ demands. Building and operating highly automated lines is not a straightforward task, especially when the bottle format is unique, and the line is being built from the ground up to accommodate the new format. In this study, downtime data and Overall Equipment Efficiency (OEE) analysis was used to determine the effectiveness of a newly built mayonnaise bottling line during the ramp-up period and the main reasons behind low OEE, a lengthy ramp-up period, and high downtime. Two pieces of machinery were the most significant contributors to downtime, a newly bought labeler, whose factory acceptance test (FAT) was never performed, and an old, repurposed drop packer, that was previously being used for a much larger packaging format. It was found that the two machines had the same MTTF (mean time to failure) value. A model was built to predict the likelihood of attainment loss using a Monte Carlo simulation after performing a goodness of fit analysis on the time-to-failure (TTF) and time-to-repair (TTR) data available. From this model, the availability of the line was determined, and the effect of the two equipment was shown to be strong on the overall performance of the line.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".