The Digital Transformation Competences for Brazilian Automotive Managers: A Transdisciplinary Engineering Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it