Statistical Analysis of Time Collection Tools for Simulation of Industrial Systems
Bibliographic record
Abstract
This research focuses on statistically analyzing the results of time collection tools for use in simulating industrial systems, employed in a Honduran banking institution.The adaptability of the instruments to changes in simulated industrial systems is evaluated, validating time reduction strategies through piloting and data triangulation.The methodology included time measurements using manual timing, Excel, and a microcontroller (ESP).Through piloting with the tools, improvements were identified prior to official data collection.As part of the results, it was found after the final data collection that the majority of the bank's clients opt for multiple services.In conclusion, it is essential to define the activities to be analyzed beforehand to avoid unnecessary data collection.After collecting the data, a statistical analysis was conducted to examine the properties of the tools used.Through tests comparing variances and means, as well as ANOVA to examine multiple samples, it was concluded that the tools perform similarly in data collection.Therefore, the selection of any of the three tools is left to the user's discretion.The statistical analysis and data simulation provided by the banking institution revealed certain peculiarities of the system used.During equality tests, an approximate delay of two minutes was noted in the banking system's time records.Additionally, the simulation indicated that the average time within the system increases by 3.89% when considering the use of the ticket machine compared to not using it.
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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) | 0.001 | 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".