Towards a Framework for Assessing the Maturity of Manufacturing Companies in Industry 4.0 Adoption
Bibliographic record
Abstract
The recent introduction of new disruptive technologies aimed at monitoring, controlling, optimizing, and automating production systems is shifting the manufacturing landscape towards a fourth industrial revolution. In this new industrial paradigm, manufacturing companies face complex challenges requiring the development of new organizational and technological capabilities. With this context in mind, this chapter is intended to provide a maturity assessment framework to understand the transformation process in manufacturing companies transitioning to Industry 4.0. The proposed framework is applied to 10 in-depth industrial case studies in Canada and Italy, two countries with increasing awareness of the Industry 4.0 revolution. A comparative case analysis revealed four different standards, or archetypes, for Industry 4.0 adoption, which are discussed and analyzed, highlighting a relationship between a company's manufacturing configuration and its path towards Industry 4.0 adoption.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".