Barriers to adopt industry 4.0 in supply chains using interpretive structural modeling
Bibliographic record
Abstract
This research aims at exploring barriers of adopting Industry 4.0 in manufacturing supply chains. Data were collected based on a review of extant literature on barriers Industry 4.0 adoption, individual interviews with a panel consisted of academic and industry experts. Following numerous previous studies, interpretive structural modeling (ISM) and matrix multiplication applied to classification (MICMAC) analysis were conducted to order 10 barriers based on their importance and impacts. The results excluded one barrier “cyber security challenges”, categorized another one as a dependent barrier “lack of digital strategy”, and eight barriers as linkage barriers “lack of infrastructure”, “personnel resistance to adopt new technologies”, “high investment requirements”, “data management and quality challenges”, “uncertainty of economic benefits”, “low maturity level of technology”, “lack of adequate skills”, and “job disruptions”. Henceforward, it was concluded that mitigating these eight barriers is very critical to ensure a successful adoption of Industry 4.0 technologies in supply chains. Further studies are required to categorize these eight barriers based on their importance and relationships.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".