Deployment of Interpretive Structural Modeling in Barriers to Industry 4.0: A Case of Small and Medium Enterprises
Bibliographic record
Abstract
Small and medium enterprises (SMEs) are vital contributors and significant drivers of any manufacturing sector. The Industry 4.0 (I 4.0) revolution has made the global economy highly competitive and automated, requiring Indian SMEs to adapt more quickly. Therefore, this study aimed to identify the barriers to implementing I 4.0, simplifying the complex interrelationship among such barriers with the help of a suitable model, categorizing them as independent and dependent ones, and, ultimately, leveling the same drivers, autonomous linkages, and dependent forces. The present investigation thoroughly examined the existing literature and summarized the list of barriers into fifteen significant barriers to the smooth establishment of Industry 4.0 in India. The identified barriers were analyzed with the help of Interpretive Structural Modeling (ISM) Diagraph and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. This study was able to explore the interrelationship among these barriers. The study has found found a lack of support from stakeholders, and insufficient managerial support emerged as a major factor neglected by Indian SMEs. However, uncertainty in the predicted demand for products, the lack of an alternate solution to the technological breakdown, and doubt about the sustainability of Industry 4.0 (relating to its potential to lead to unemployment in society, etc.) are significant contingent barriers. These barriers can impact the other strategic choices related to the successful implementation of Industry 4.0. This study’s observations can help decision-makers make strategic decisions to manage the barriers affecting Industry 4.0 in Indian SMEs. This research revealed a scope that can be extended to other South Asian and developing nations. The results of the present work can be further studied with structural equation modeling (SEM) and multiple regression analysis (MRA).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".