Productivity improvement using different lean approaches in small and medium enterprises (SMEs)
Bibliographic record
Abstract
Small and Medium Enterprises (SMEs) play a crucial role in the Indian economy. To remain competitive in the global market, application of Lean Manufacturing Techniques (LMT) helps SMEs to improve their processes in alignment with customer needs. The purpose of this study is to reduce overall lead time by identification and eliminating non-value added activities (NVA) from the manufacturing system. In this paper, the current manufacturing processes have been thoroughly studied using the principle of work study. Several lean approaches such as- TAKT time computation, Value stream Mapping (VSM), layout optimization, Kanban, Andon etc. have been used to make improvement in line efficiency and hence productivity. The study shows that use of line balancing technique, matches the cycle time with calculated TAKT time. And the efficiency of the line has increased approx 10% and hence balance delay got reduced. Again VSM helps to reduce the lead time of foundry shops by 51 minutes. It is one of the few studies that deal with productivity improvement in Indian SME.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".