The role of intelligent manufacturing systems in the implementation of Industry 4.0 by small and medium enterprises in developing countries
Bibliographic record
Abstract
Abstract The desire to enhance connectivity and communication while simplifying data used to improve and optimize products and processes has driven many organizations within developed countries to invest heavily in implementing intelligent technologies for manufacturing. These technologies promise to enhance global manufacturing capabilities while sustaining demands by integrating equipment and frameworks in advanced economies for future production systems. On the other hand, many small and medium enterprises (SMEs) within developing countries have shown apprehension and mistrust toward the emerging technologies associated with Industry 4.0. This article provides a comprehensive review of SMEs' readiness within developing countries to implement the novel technologies falling within the Industry 4.0 realm. Such techniques include intelligent manufacturing systems, cyber‐physical systems, and other crucial technological tools for improved connectivity and communication within manufacturing and production systems. Analysis of the literature shows that many SMEs within developing countries are experiencing delays in introducing intelligent manufacturing and digitizing factories due to a lack of knowledge and communication issues. These firms lag in embracing the transformation to equipment and systems that can communicate with future‐oriented technologies and introduce intelligent devices and machines into production processes. This article explores challenges, identifies gaps and suggests the potential solutions to address the readiness of SMEs toward Industry 4.0 in developing countries, through a systematic summary and integrative analysis of the findings from the literature.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".