Aplicação da metodologia de análise e solução de problemas (MASP) na logística de uma empresa do setor agroindustrial / Application of the analysis and problem solving methodology (MASP) in the logistics of a company in the agroindustrial sector
Bibliographic record
Abstract
O objetivo do trabalho é aplicar o Método de Análise e Solução de Problemas (MASP) no setor logístico de uma empresa de grande porte do setor agroindustrial. A empresa comercializa insumos, máquinas e implementos agrícolas. A ideia central do estudo é analisar os problemas existentes no processo, observar e identificar as causas mais impactantes de acordo com os procedimentos da metodologia. O método utilizado para realização da coleta dos dados foi à observação direta com os colaboradores e responsáveis pelos processos da logística da empresa. Foram identificados cinco problemas na Logística e o principal foi o atraso na entrega com o maior nível de priorização. Após a identificação deste problema, as etapas do MASP foram aplicadas, visando à investigação das causas e a elaboração de um plano de ação para bloquear as causas fundamentais diagnosticadas. O MASP e as ferramentas da qualidade utilizadas podem ser destacados como aplicações úteis para melhorias de processos e foram extremamente significativas para atacar o principal problema da empresa estudada.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".