Collaborative approach to digital transformation (CADT) model for manufacturing SMEs
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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".