Production Model Based On Lean Manufacturing and Systematic Layout Planing To Reduce Waste in a Company in the Poultry Sector: A Case Study
Bibliographic record
Abstract
This work performs data analysis, as well as economic evaluation to validate whether the implementation of the engineering tools to be used is viable.For this purpose, the background, state of the art of the project, analysis and diagnosis of the problem were prepared using different improvement tools, which in this case are: 5's, visual control and Systematic Layout Planning (SLP).In addition, solution proposals were designed and developed for each tool and in this way validate the solution and economic validation; the impacts they may have on the different stakeholders relevant to the company and the sector.Thus, to achieve the results, pilot tests of 5's and visual control were developed.Where when carrying out an initial audit the result was 62% and after carrying out the pilot test there was an improvement that reached 86%; What it reflects is that if the company complies with the tools, there will be greater efficiency in it.Similarly, the use of the Arena simulator for the SLP tool also improved the technical gap approximately from 0,96% to 0,82%; However, the sector indicates that the ideal is 0,5%, but this will be achieved as the company adapts to the new changes.In addition, the environmental impact was reviewed and its importance was reviewed in order to mitigate it and achieve a friendly product.Likewise, by implementing these Lean Manufacturing and SLP tools, the monthly margin percentage increases by 2%, since monthly by reducing internal shrinkage there would be a monthly income of approximately $1 716,82 and if unplanned stops are reduced using visual management, you will have an income of approximately $4 539,86.For all of the above, it was concluded that, if the poultry company implements engineering tools, it will be more profitable, it will achieve cleaner spaces and, above all, collaborators more committed to their work.
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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".