Operationalization of Critical Success Factors to Manage the Industry 4.0 Transformation of Manufacturing SMEs
Bibliographic record
Abstract
As an increasing number of manufacturing small and medium enterprises (SMEs) tackle their digital transformation toward Industry 4.0, the need for a methodology to manage this transformation, tailored to their particular context, becomes apparent. Since recent studies have identified critical success factors (CSFs) for the Industry 4.0 transformation of manufacturing SMEs, this paper aims to operationalize these CSFs and propose an Industry 4.0 transformation management methodology. This research is based on an extensive literature review on CSFs for Industry 4.0 transformation, followed by a Delphi–Régnier survey with a panel of Industry 4.0 experts. For each CSF, specific actions to perform at different stages of the Industry 4.0 transformation were defined and validated by experts. Based on a proposed Industry 4.0 transformation process, not all CSFs have to be managed at every phase and step of the transformation process. Each CSF must be supported by different actions positioned within each Industry 4.0 transformation process step. The results of this research are particularly relevant for manufacturing SME managers and consultants managing Industry 4.0 transformation. By performing these actions, they can ensure the achievement of multiple CSFs during their digital transformation projects and, thus, ensure their success. This research combines the academic and professional domains by proposing a way for theoretical findings to be translated into clear actions. The proposed model allows all the actors involved in manufacturing SMEs’ digital transformation projects to understand the actions needed to achieve a successful transformation.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".