Prerequisites for the Implementation of Industry 4.0 in Manufacturing SMEs
Bibliographic record
Abstract
Technologies associated with the Fourth Industrial Revolution have demonstrated a significant impact on the productivity and agility of manufacturing companies, enabling them to be more competitive. The implementation of Industry 4.0 allows these companies to be better equipped to meet mass customization requirements. In Quebec, small and medium-sized enterprises (SMEs) in the manufacturing industry don’t typically adhere to this technological trend, which creates a performance gap between them and their competitors. One of the main reasons Quebec struggles to keep up is that its SMEs do not seem to be equipped to make this digital transformation. The purpose of this paper is to identify, within a literature review, the prerequisites necessary to prepare manufacturing SMEs for the digital revolution. This review highlights different authors’ work to identify the most common prerequisites that are known. The results will help guide manufacturing SMEs to better prepare their readiness to implement Industry 4.0 and begin their digital transformation. With the results obtained from the research, combined with the design of experiments and a Monte Carlo simulation, it will be possible to validate the prerequisites. This will be done by implementing them in an aluminum product manufacturing SME in Quebec.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".