Análise de benchmarking para projeto de plataforma logística
Bibliographic record
Abstract
The use of logistic Platform in the world has grown considerably, but in Brazil, this practice is still incipient. Given local circumstances, projects need to be well adapted to Brazilian reality, there are few studies that help in this direction. The aim of this paper is to suggest guidelines for strategic planning of the new logistics platform to be installed in Campinas, So Paulo. As a starting point of the study were selected twenty-nine logistics platforms around the world and, through Data Envelopment Analysis (DEA) have been identified that constitute the global benchmarking and were taken as the base platform design logistics of Campinas (PLC). For this analysis we adopted the DEA-BCC model (Variable Return to Scale-VRS) with the following performance indicators, areas of logistics platforms, the capital invested, number of businesses attracted and annual cargo handling. After the initial assessment of the twenty-nine platforms by DEA were identified seven as global benchmarks and this set three references have been identified as best practices for the PLC (Atlantic Gateway-Halifax Logistics Park, Canada, Raritan Center, USA; Rickenbacker Global Logistics Park and USA). At the same time a qualitative study, using the method of multiple case study, identified five more platforms PLAZA (Span), Distrito de Nola (Italy), Rugis (France) Bremen GVZ (German) e Dallas logistic Hub (USA) that could be included as references for the enterprise. From a detailed analysis of selected platforms were able to identify suggestions for improvements and design guidelines for the Brazilian platform. This application demonstrated that the proposed strategy has wide applicability and gives good results for project evaluation design of logistics platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".