Obstacles of Implementing Industry 4.0 in Nepalese Industries and Way-Forward
Bibliographic record
Abstract
Industry 4.0 is buzzword in recent years and has become a topic of growing importance. It is a new technical framework that has been widely debated and studied and is likely to eventually constitute a fourth industrial revolution because it provides significant progress relevant to intelligent and potential industries in the market. As Nepal has introduced open policies for improving trade conditions in the mid-1980s, industries have to be competitive and capable enough to sustain themselves in such open policies. Such, dependencies can be minimize, and could only be possible, through the increasing the competitiveness of Nepalese industries with the help of use of new technologies. In such context, Nepalese industrial readiness for industry 4.0 is important topic to discuss. This study aims to identify the obstacles of implementing industry 4.0 in Nepalese Industries lies within 3 industrial estates of Kathmandu Valley i.e. Balaju, Patan and Bhaktapur industrial estates. Data has collected data from all 287 running industry from all three industrial estates with the help of questionnaire through respondent interview using KoBo Collect Toolbox. Our study finds that half of the industries (49%) face hurdles while adopting new technologies. Among them, the major hurdles are lack of infrastructure, lack of skilled manpower, lack of capital, poor implementation of policies. Among two third of the respondents think obstacles in implementing industry 4.0 is manageable. Political support, improvement in implementation mechanism and long term strategy are key factors that support industries to invest in new innovative technologies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".