Bibliographic record
Abstract
Manufacturing enterprises are currently facing many challenges of industrial revolution 4.0 when beginning to change the production model, working with embedded systems which can collect and transmit specific data. The application of maturity models to the Industry 4.0 may help organizations to address the challenges regarding the concept of Industry 4.0 and the diversification methodology. This research is going to examine the application of a maturity model for assessing Industry 4.0 created by Schumacher et al. (2016). The Maturity model of Schumacher et al. (2016) was built to assess the maturity and to infiltrate of the enterprises manufacturing into the Industry 4.0. The purpose of this paper is examining the applicable of using the Maturity model in evaluating the maturity of industrial enterprises in Vietnam toward industry 4.0. The issues will be addressed in this paper: i) The nature of Industry 4.0 and challenges for the manufacturing enterprises; ii) The Maturity model for manufacturing enterprises towards Industry 4.0; iii) The process of applying Maturity model in assessing the manufacturing enterprise towards Industry 4.0; iv) Some initial recommendations for Vietnamese manufacturing enterprises and v) Conclusion.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".