Priorización de problemas en talleres metalmecánicos: dos casos de estudio en Boyacá-Colombia
Bibliographic record
Abstract
En los procesos de mejora continua la identificación y priorización de problemas es el punto clave de partida. En este trabajo, se presentan dos metodologías para la identificación y priorización de problemas en talleres del sector metalmecánico de BoyacáColombia, con el fin de aportar al mejoramiento continuo de los mismos. Para cumplir con el propósito de esta investigación se seleccionaron dos talleres metalmecánicos, identificando y priorizando los problemas vitales presentes al interior de sus procesos productivos a través del ciclo PHVA y el Análisis de Vulnerabilidad de Procesos (AVP). Se concluye que la priorización de problemas bajo las metodologías propuestas y el compromiso de la alta dirección es una etapa vital para la mejora continua de talleres y organizaciones empresariales.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".