The Impact of Concurrent Engineering Techniques on Assembly Line Redesign: An Applied Study
Bibliographic record
Abstract
The research aims to use the latest technology, namely "Concurrent Engineering ", to redraw production and assembly lines to be more efficient and take less time in manufacturing and delivery.Which will help to earn more money, reduce the cost, achieve quality standards, and continue the production line in the assembly lines of the General Company for Mechanical Industries / Automotive Assembly Line, calculating the workstations and stages until the final production and presenting it to the customer.It was noted that there were multiple problems that led to losses on the assembly line as a result of the lack of distribution and timing of activities across the stages of the production line, causing bottlenecks in some workstations.This was reflected in a loss of about (398) minutes at a cost of about $3,900 per minute, which led to insufficient use of human or material resources (machinery and equipment).The current study presented a proposal through which the assembly line can be restructured and the implementation of some activities that are close in completion time can be synchronized, which in turn leads to reducing the total completion time from (937.5 to 772 minutes), that is, by 16.75%, in addition to maximizing working times and machine operating efficiency.And reduce wasted time at most stations.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".