Challenges in Adopting Industry 4.0 for Indian Automobile Industries: A Key Experts’ Perspective
Bibliographic record
Abstract
Industry 4.0 has revolutionised manufacturing, presenting significant challenges for adoption, particularly in developing countries like India.This study identifies and evaluates challenges specific to the Indian automobile industry's implementation of Industry 4.0 to address this.Leveraging Latent Dirichlet Allocation (LDA), a machine learning-based text analysis algorithm, we discerned challenges from existing literature.Subsequently, employing the Delphi method, we refined these challenges, leading to a questionnaire-based survey and fuzzy Decision-Making Trial and Evaluation Laboratory (f-DEMATEL) data analysis to prioritise them.Our research framework involved collaboration with original equipment manufacturers (OEMs), suppliers, and academic experts who ranked 20 challenges by influence.Findings reveal divergent perspectives: OEM experts highlight concerns regarding outdated infrastructure, high initial costs, financial uncertainty, and a lack of strategy and standards.Supplier industries emphasise the importance of Information Technology and Research & Development departments, the maturity of Industry 4.0 tools, industry-academia collaboration, and addressing strategy and standards gaps.Academia underscores the need for financial support, government assistance, and organisational adjustments.These insights offer crucial guidance for managing Industry 4.0 challenges in the Indian automobile industry, facilitating targeted and practical implementation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".