Beneficios y limitaciones del modelo ABC : análisis de un caso del sector logístico mexicano
Bibliographic record
Abstract
Resumen El objetivo de este trabajo es conocer los beneficios y las limitaciones del modelo de costos basado en actividades (ABC) y su aplicacion en el sector logistico mexicano. Se revisaron estudios precedentes sobre ventajas e inconvenientes de dicho modelo, para contrastar los resultados en un estudio de caso. Se concluyo que el exito del ABC esta positivamente relacionado con el apoyo de la alta gerencia y su empeno por implicar a los miembros de la organizacion, lo que constituye el principal factor critico de exito del ABC; otro es la participacion y el soporte del personal de sistemas de informacion; asi como que se necesita la utilizacion paralela de otras herramientas estrategicas de mejora de procesos. palabras clave: ABC, beneficios y limitaciones, estudio de caso. Abstract This paper is aimed at identifying and describing benefits and limitations of the cost model based on ABC activities, and its application in the Mexican logistic sector. In order to prove the result of the study case, previous literature on model´s advantages and inconveniences was consulted. As a result, it was demonstrated that ABC´s success is positively linked with the support coming from high level administration, and its commitment to involve the members of the organization –such is the main fact for ABC success– as well as the participation and support of information system´s staff; also, it was confirmed that ABC model´s need the parallel use of other process improvement strategic tools. keywords: ABC, benefits and limitations, study case.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".