Plan de mejora de la productividad de los procesos de una plataforma de cobranzas de la compañía telefónica Gestión de Servicios Compartidos
Bibliographic record
Abstract
Union Telecom is a Telecommunications project, offering long distance services nationally and internationally that allows users to make calls from the US and Canada to any country, this is contracted by the group of Telefonica del Peru. \nThis company has a billing department, which has a collection method called sectorization, based on the distribution database for each collection agent at random, this makes the call agents according to their convenience to reach their own goals no matter the treatment, and not according to customer behavior, this situation has generated a climate of dissatisfaction for both customers and agents, reflected in high rates of resignations in the service and poor collectibility. As an engineer I could not be based on perceptions, so I used industrial engineering tools to collect information, calling it preliminary audit, where we got the information to know that the problem was the database distribution of sectorization, however the current software was not able to redistribute the database according to the requirement, so we decided to implement a new enterprise software with the ability to not only distribute the database, but other long-term benefits. After a detailed feasibility study, it was decided to implement it. After two months, we undertook a second audit for the comparison with the first one having a conclusion, where we got better results after the implementation of enterprise software.
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.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".