The Digital Transformation Competences for Brazilian Automotive Managers: A Transdisciplinary Engineering Approach
Bibliographic record
Abstract
New technologies related to Digital Transformation (DT) and the Industry 4.0 (I4.0) modify the way business and productive processes are carried out, generating complex changes for industry and engineering, establishing new tasks and human roles, and interacting with the characteristics of Transdisciplinary. Digital engineering managers play an integrative role by relating and using the organisation’s digital technological knowledge to generate better business results. The characterization of managers’ competences to guide and stimulate value creation in industrial sectors is still not sufficiently investigated and emerges as a critical element for industrial development in the digital age. This research fulfils this gap and aims to rank four types of necessary competences for engineering managers facing the DT/I4.0 in the automotive sector. The methodological approach adopted is quantitative, based on the judgement of engineering managers from the Brazilian automotive sector, which is globally representative in terms of productivity. An Analytic Hierarchy Process (AHP) is applied in the data treatment. Results are based on a sample of 35 interviews from six automotive companies with different levels of complexity in production operations and formal programs for DT/I4.0 implementation. Findings indicate the relative priority for the digital technical, managerial, social, and motivational competences, presenting insights with implications to guide the development of the digital engineering managers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".