Implementation of the 5S methodology to improve efficiency in a pastry microenterprise San Ramon - Chanchamayo
Bibliographic record
Abstract
In Peru, the majority of pastry shops belong to micro and small companies, one of the problems they face is the deficiency of the production line due to the lack of order, cleanliness and good habits of the collaborators, so the objective of this article is to present the implementation of the lean manufacturing tool, which is the 5S method, which was implemented in the production area of a pastry company for a period of 4 months, the results are as follows: search time for pastry utensils and instruments reduced by 75%, 8 square meters recovered, marking 62% and organization of supplies 42%.A notable change was experienced: the meticulous organization of tools and materials, along with the standardization of processes, improved the distribution of cream filling, reducing defective products by 75% and increasing monthly revenue by 15%, reduction of 30 % in costs due to waste of materials and reduction in cleaning time to 15 minutes per day.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".