Implementation and introduction barriers of industry 4.0 with canadian lean mature aerospace manufacturing companies
Bibliographic record
Abstract
The manufacturing process has dramatically altered during past years and lean is one of the methodologies that played an important role in alterations. Lean is a revolutionary methodology that emerged in the automotive industry. Lean companies succeed in decreasing costs by removing waste. Gradually, new methodologies became popular in different industrial sectors. Industry 4.0 is another innovative method that aims to drive the manufacturing process to the higher levels via using information technology. \n \nNowadays, some companies combine lean and industry 4.0 to achieve higher level products. Canadian companies, especially aerospace companies are highly motivated to use new technologies, as it is apparent in the 2015 Activity Report of Aéro Montréal. In 2015, They aimed to implement new strategic and operational framework for doing projects to get aero Quebec aerospace sector to a higher level. Therefore, a number of aerospace manufacturing companies in Canada implement industry 4.0 and some of these companies adopt lean too. Firms face a vast domain of barriers before implementing industry 4.0 and during the process, such as high implementation cost, lack of facilities and proper approaches, and lack of knowledge about industry 4.0. This thesis aims to investigate the tendency to industry 4.0 among lean Canadian aerospace manufacturing companies. Also, the obstacles which companies face before and during the implementation of industry 4.0 will be evaluated.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".