Analysis of Time Measurement Strategies in the Automotive Components Industry Using Design Science Research
Bibliographic record
Abstract
The digitization of production processes is an important factor when considering the development scenario of advanced manufacturing. For companies to start this development, their processes need to be digitalized, and this is a growing demand. In this sense, this work aims to analyze the digitalization of time measurement activity in industrial processes, which is also known as Time Study or Chronoanalysis. Thus, the purpose was to analyze the use of video technologies as a support for people who are responsible for carrying out time measurements in industrial activities. This analysis aimed at the automotive industry, in application to a manufacturer of structural components of automotive vehicles. The Design Science Research (DSR) method was applied to identify the most critical process of the company, and in this activity to carry out time measurements using a video reading software called Quick Time Player® from the company Apple®, with a proposal to analyze the micro automatic movements of the machine, helping to identify movements that could be eliminated or have reduced times, generating cycle time reduction and increased production capacity. Finally, it was identified that there were benefits in the proposed method when comparing the analysis of time measurements with the use of the software and the company's usual methods, resulting in greater precision of chronoanalysis and user satisfaction with the new method.
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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 | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".