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Record W4385689898 · doi:10.52080/rvgluz.28.e9.29

Gestión de inventarios en microempresas del sector farmacéutico, Colombia

2023· article· es· W4385689898 on OpenAlexaff
Elizabeth Palma Cardoso, Dixon Gerley Acebedo Molina, Ruth Erika Morales Lugo, Robert Alexander Guzmán

Bibliographic record

VenueRevista Venezolana de Gerencia · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicTechnology in Education and Healthcare
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

El inadecuado manejo de los inventarios afecta el crecimiento económico de las empresas del sector farmacéutico, reflejándose en una toma de decisiones alejada de las necesidades reales en este sector empresarial. El presente artículo de investigación pretendió identificar elementos de la gestión actual de inventarios en microempresas del sector farmacéutico del Tolima, Colombia, permitiendo establecer medidas que faciliten un proceso de gestión partiendo de la necesidad o problemática que originan las prácticas empíricas de sus administradores. Este estudio se trabajó a partir de un enfoque cualitativo explicativo y un método inductivo para analizar las particularidades en el manejo de inventarios estableciendo parámetros de control necesarios en el manejo de los inventarios. Como muestra se tomaron 286 microempresas del sector farmacéutico del Tolima, registradas en cámara de comercio. Como resultados de la investigación se encuentran en primer lugar, un diagnóstico que permitió determinar los factores que deben ser optimizados dentro de los procesos empresariales y finalmente, el desarrollo de la propuesta de control de inventarios, basado en la combinación de los modelos japoneses Just Time y 5S. Se tienen en cuenta los hallazgos encontrados en el control y manejo de inventarios por parte de sector empresarial.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.038
GPT teacher head0.374
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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