Estrategias de Mejora en Destilería Montreal: Un Enfoque QFD para el Posicionamiento Competitivo en Bebidas Alcohólicas
Bibliographic record
Abstract
Quality analysis in any field aims to identify the fundamental characteristics for both producers and consumers, enabling a comprehensive evaluation of the production process. The artisanal production of liquor is closely tied to quality control, as control standards must be in place from the very first stage to ensure compliance. This is essential both because it is a consumer product and because such standards are necessary to obtain commercialization permits. In the city of Cuenca, Ecuador, this market is experiencing growth, with an increasing contribution from both artisanal and industrially produced liquors. The research focuses on the Capulí Liquor Product as its flagship, which holds significant market potential. For the analysis, the House of Quality methodology within the framework of Quality Function Deployment (QFD) was employed. This approach allowed for the identification of essential quality attributes as determined by liquor experts, namely: aroma, alcohol content, color, price, and density. The findings revealed that, for these variables, the studied venture demonstrates lower quality compared to its competitors. Based on this, specific recommendations for improvement have been established.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".