Evaluation of lean practices in warehouses: an analysis of Brazilian reality
Bibliographic record
Abstract
Purpose This article aims to investigate the most applied lean warehouse practices in Brazilian warehouses. Design/methodology/approach To perform this research, three phases were conducted: a literature review, a multiple case study, and an analysis of lean warehouses practices implementation by an engineering committee. Thus, both qualitative and quantitative approaches were used. Additionally, the study has an applied nature, with an exploratory and descriptive character. Findings Results showed that regardless of the type of criterion used, the most implanted practices are those that do not involve investments in technology. On the other hand, practices like RFID and Cross Docking systems were not found in any of the operations, which shows numerous possibilities for improvement. Originality/value The main contribution of this article is to initiate a debate about the management and productivity of Brazilian warehouses, a theme still little explored by the academic community despite the importance that the logistic scenario represents for Brazil as an emerging country and leader in Latin America, participating actively in several global supply chains.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| 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) | 0.001 | 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".