Implementation of an Industry 4.0 Strategy Adapted to Manufacturing SMEs: Simulation and Case Study
Bibliographic record
Abstract
Quebec’s small- and medium-sized enterprises (SMEs) in the manufacturing field are facing a major challenge: implementing a successful digital transformation in an increasingly competitive world, with a labor shortage and customer demand for highly customized products. Technology is a leading solution for improving competitiveness. However, the tools and subsidies available offer little in terms of results for these companies, which have neither the prerequisites nor the resources to successfully carry out their digital transformation. This research aims to develop an adapted Industry 4.0 strategy for manufacturing SMEs reorienting themselves toward mass customization. It seeks to demonstrate that agility and modular design are prerequisites, and it advocates for individual assessments as success factors. The research presents the development of such a strategy for manufacturing SMEs. A case study in the form of action research, combined with a simulation-based experimental design based on a sample of one Quebec manufacturing SME, serves to validate the implementation of the adapted strategy. This research emphasizes the importance of lean, agility and modular design concepts and of individual assessment for successful Industry 4.0 implementation in SMEs. Future research could systematize modularity management in the Industry 4.0 era to boost SME competitiveness.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".