Reconciling digital transformation and sustainability: Towards a tailor-made strategy for manufacturing SMEs
Bibliographic record
Abstract
This paper addresses the lack of a comprehensive model for assessing the benefits of Industry 4.0 (I4.0) in small and medium-sized enterprises (SMEs), considering their diverse priorities and objectives. SMEs struggle to balance economic growth and environmental constraints, amid tightening regulations. Existing digital performance models focus on technology integration but rarely align outcomes with SMEs’ strategic goals, particularly regarding environmental performance. This study proposes an adaptable model that integrates digital performance dimensions with sustainability indicators, aligning with the Triple Bottom Line (TBL) framework to evaluate the economic, social, and environmental impact of digital transformation in SMEs. Using data from 30 Quebec-based SMEs and hierarchical clustering, we identify groups of companies sharing similar operational realities, resources, and objectives. Clusters inform model customization. The proposed model thus measures I4.0’s effects on economic, social, and environmental aspects, providing a structured approach to prioritize digital transformation initiatives.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".