Potential COVID-19 impacts on the transition to Industry 4.0 in the Brazilian manufacturing sector
Bibliographic record
Abstract
Purpose The purpose of this viewpoint is to present some reflections about the coronavirus disease 2019 (COVID-19) pandemic impacts on the transition to Industry 4.0 in the Brazilian manufacturing sector context. Design/methodology/approach Initially, a bibliographic research study was carried out to establish a theoretical background and contextualization. After analysing different kinds of documents, the authors of this viewpoint discussed potential COVID-19 impacts on the transition to Industry 4.0 in the Brazilian manufacturing sector. A multidisciplinary discursive approach was used in the debates. Findings The COVID-19 pandemic will negatively influence the transition of Brazilian manufacturing sector to Industry 4.0. Despite the fact that some “World Class Companies” based in Brazil still continue the transition process towards the “Digital Revolution”, most of Brazilian manufacturing companies are postponing important initiatives related to Industry 4.0 due to uncertainties. In addition, policies promoting innovation are increasingly necessary. Practical implications This viewpoint presents interesting implications for researchers and society. Researchers can use these reflections to structure surveys or case studies to better understand the aforementioned impacts on companies due to the pandemic. These reflections can also be used by society for public policy debates. For companies, the information presented highlights the relevance of Industry 4.0 as an important phenomenon to manufacturing sector and companies' competitiveness. Originality/value This viewpoint presents reflections which may be used to encourage debates about how to manage digital transformation in the manufacturing sector during an unstable environment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".