Evaluation of the influence parameters of Industry 4.0 and their impact on the Quebec manufacturing SMEs: The first findings
Bibliographic record
Abstract
The digitalization of industries is at the heart of today’s global economy. However, there seems to be confusion about the most effective methods for initiating this transformation, and even more so for the manufacturing Small and Medium-sized Enterprise (SME). In a context of labor shortages, globalization and access to goods, services and skills everywhere and at any time thanks to the Internet, the need to stand out from the competition becomes a crucial issue. This research attempts to evaluate and identify the most effective ways to facilitate the digitalization in a context of manufacturing SMEs. Thanks to the measure of the digital performance and an 80-hour experience-based methodology using a questionnaire and field interviews, the determining factors of influence of the digital transformation could be raised. This paper uses a model of digital performance and hypothesis testing to try to identify the business practices and the 4.0 technologies that have the greatest effect on the performance of manufacturing SMEs. The results then intents to guide the efforts both in academia and in the field concerning digitalization of SMEs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".