Simulation model for packaging time optimation using Lean Manufacturing
Bibliographic record
Abstract
Today, the consumption of wheat flour in Peru is 2 million tonnes per year, but the country does not produce enough wheat for this demand, therefore, imports cover around 92% of this requirement, the main suppliers being Canada, the United States and Argentina. All this indicates that the flour industry will continue to grow, as is the case of the company GRUPO INGENIA-T, one of the few successful Peruvian industries of milling, dosing and packaging of wheat and other raw materials for consumption. However, when monitoring and analysing the manufacturing conditions and total process flows, production problems were detected and simulated in the Anylogic software where the Lean Manufacturing methodology was applied to remedy the bottleneck, giving us the working time of the staff, which is 8 hours and 10 minutes. Then, the present study aims to implement a simulated model to optimise production times in accordance with the criteria and requirements offered by the Theory of Constraints, which is why it was essential to establish a flow diagram of the industry’s processes. Finally, we have as a result that the production of 70 bags well packed and sealed was given in a time of 1 to 2 seconds for the inspection and cleaning of the same, without the coupling of the proposed system would not be possible to reach such a quantity because the people in charge of this procedure need to rest because the work they do is done manually, therefore, we conclude that the machine complies with reducing time and increasing production.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".