Artificial Intelligence as a Process Optimization Driver under Industry 4.0 Framework and the Role of IIoT, a Bibliometric Analysis
Bibliographic record
Abstract
Connected products generate data that are being seen as a key source of competitive advantage, and the management and processing of that data are generating new challenges in the industrial environment. This paper proposes a conceptual framework and preliminary findings to go deeper into a bibliometric analysis around the idea of artificial intelligence and machine learning (AI/ML) as a tool for the optimization of processes within the Industry 4.0 model. Methodologically, a technological mapping was carried out through an exercise on Scopus indexed database, the results of which were analyzed using bibliometric indicators. The bibliometric study is completed with a screening of relevant papers searching for linking among the industrial Internet of Things (IIoT) or Internet of Things (IoT), IA/ML and process optimization. Finally, this paper gives information about the state-of-the-art AI/ML applied to the optimization of industrial processes and presents a novel Canadian startup that has a business model that aims to make AI/ML easy to use in the industrial world towards a lean processes strategy.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.002 |
| 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".