Simulation model for packaging time optimation using Lean Manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it