La gestión de procesos de pedidos en el sistema SAP y su influencia en la atención al cliente de la empresa La Viga S.A. en el último trimestre del año 2016 en Lima
Bibliographic record
Abstract
All organizations are linked to satisfy the customer, so timely care is the main reason and engine of an organization, which every process that has to be measured has to be measured in order to satisfy the final consumer, so it is necessary to provide the customer with the you need to enter your orders into our system in a timely manner. The following research is intended to support "The management of order processes in the SAP system and its influence on customer service in the company LA VIGA S.A. in the last quarter of 2016 in Lima." \nIt is about analyzing the flow of the generation process orders to the SAP system and the coordination for its programming, which serves to verify that the attention was timely, determining that the Back-Office process is focused on the order management in the SAP system in the last quarter of 2017. \nThe results of the present project prove that the responsibility of the Back Office in the process of order management in the SAP system is very important and influences the final attention to the customer who is the one who receives the order in the requested time.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".