Modification of a Plant's Engine Assembly Line to Reduce Employee Movement and Increase Productivity
Bibliographic record
Abstract
Modification of the workstations of the automaker engine assembly line in the Betim/MG unit, with a focus on ergonomics, reducing employee movement during the implementation of activities by eliminating activities that do not add value to the product. The entire project was structured according to WCM (World Class Manufacturing) methodology that consists of concepts, principles and techniques for managing operational processes, inspired by the Toyota Production System (TPS), which focuses on wastage elimination. The WCM is supported by technical and management pillars and the one used in this study was the WO (Workplace Organization) that seeks to create an ideal workplace to achieve maximum safety, improved quality, and maximum value in product transformation. The premise for the development of the solutions was the minimum use of resources, prioritizing simplicity of the devices by applying concepts of low cost automation (LCA) leaving the material near the assembling point. The results were workstations with balanced assembly times, thus increasing productivity by 30%, providing greater safety in the activities of the operator and a reduction of 39% in the activities that add no value to products.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".