Logistic management and neural network maps: Keys to cost optimization in cardboard packaging manufacturing
Bibliographic record
Abstract
The focus of this research is to analyze how supply chains’ management affects production costs in the cardboard and Packaging sector in Peru, specifically through the creation of artificial neural networks (ANN) to improve the logistical activities. Non-experimental quantitative design was applied, collected the data from the Year 2020 to the Year 2024 and sought to assess variables such as supplier capacities, stocks held, bottom line costs incurred and stock out ratios. The study revealed that there exists a proportionate inverse relationship between the logistical costs and production costs, proving that as the cost of acquiring goods needed for production as well as the cost of keeping and managing stock decreases, the overall production cost also decreases significantly. The ANN model was able to perform cost predictions with a high degree of accuracy which points out the relevance of sophisticated instruments in the shift of the supply chain. Also, it is important to note the core contribution of the research – effective logistics management is emphasized as a way of increasing competition in industries where supply chains are of critical importance. This research reinforces the effectiveness of designing ANN in minimizing costs, while adding knowledge to the reporting practice of the companies aimed at bettering their costs. The results are a good contribution in terms of technological change in logistics aimed at helping the organizations remain flexible in a changing economy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".