Productivity Improvement In An Automotive Workshop Through Lean Manufacturing Methodology
Bibliographic record
Abstract
This research study focused on improving the productivity of an automotive workshop in Tegucigalpa, Honduras, by analyzing and identifying the causes of delays and implementing process improvements by applying the tools and principles of the Lean Manufacturing methodology using a quantitative approach with a descriptive scope.The problem for the automotive workshop lies in the long maintenance service times for the engine oil change, from the time the vehicle enters the workshop until it is delivered to the customer.The first step was to analyze the company's current situation and identify opportunities for improvement.Analyzing a probability sample of 3 employees in the maintenance department.The activities with the greatest negative impact on the process were analyzed, including unnecessary materials in the work area, the accumulation of waste, unnecessary transfers, and the fact that there is no person in charge of maintaining order and cleanliness in the area.The operation of the workshop was then described using indicators to improve the overall performance of the company.Seven activities were reduced in the current analysis using process diagrams and flowcharts.The duration of the maintenance service was reduced by 36 minutes, identifying a 40.6% opportunity to improve the service and standardize the process.Finally, socialization was carried out with the company, where information was shared about the project and possible implementation and execution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".