Collaborative approach to digital transformation (CADT) model for manufacturing SMEs
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
Purpose This paper aims to propose a collaborative approach model developed based on observations of two aerospace manufacturing small and medium-sized enterprises (SMEs) pursuing their digital transformation toward Industry 4.0. Design/methodology/approach This research focuses on two manufacturing SMEs in North America, and data were collected using longitudinal case study and research intervention method. Data collection was performed through observation and intervention within the collaborative projects over 18 months. Findings A model of a collaborative approach to digital transformation (CADT) for manufacturing SMEs was produced. Based on the study findings, the collaboration manifests itself at various stages of the transformation projects, such as the business needs alignment, project portfolio creation, technology solution selection and post-mortem phase. Research limitations/implications Research using the case study method has a limitation in the generalization of the model. The CADT model generated in this study might be specific to the aerospace manufacturing industry and collaboration patterns between manufacturing SMEs. The results could vary in different contexts. Practical implications The proposed CADT model is particularly relevant for manufacturing SMEs' managers and consultants working on digital transformation projects. By adopting this approach, they could better plan and guide their collaboration approach during their Industry 4.0 transformation. Originality/value This research provides a new perspective to digital transformation approaches in the aerospace industry. It can be integrated into other research findings to formulate a more integrated and comprehensive CADT model in industries where SMEs are significant players.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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