Optimizing OEE in a Peruvian Fishmeal Factory Through TQM, TPM, and Standard Work: A Case Study
Bibliographic record
Abstract
The Peruvian fishmeal industry faced persistent challenges from machinery failures, process variability, and lack of standardization, which limited its competitiveness.While previous studies explored TPM, TQM, and Standard Work individually, few addressed their combined application in fishmeal production-an existing research gap.This study addressed that gap by developing an integrated model to optimize the production of "Super Prime" fishmeal.The intervention focused on increasing equipment availability, reducing reprocessing, and standardizing key operational tasks.After implementation, OEE rose by 7.12%, MTBF increased by 7.5 hours, and MTTR decreased by 6.3 hours.The share of Super Prime fishmeal improved from 5% to 12%, and the cost-benefit ratio reached 8.83.These results provide practical insights for process-based industries seeking to enhance product quality and operational efficiency under resource constraints.The study offers a replicable framework for the fishery sector and highlights the potential for increased profitability through coordinated quality and maintenance strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".