Improving Throughput and Cost Efficiency with Lean Tools: A Business Case Study on Value Stream Mapping
Bibliographic record
Abstract
The single-minded pursuit of productivity and efficiency in the manufacturing industry has forced organizations to embrace lean tools, one of which is Value Stream Mapping (VSM). The study explains a real-world use of VSM in a pulley production plant, intended to reduce the manufacturing downtime, limit Non-Value-Added (NVA) operations, and improve the Overall Equipment Effectiveness (OEE). A major point of bottleneck was identified to exist between the transition stage between Vertical Machining Center (VMC) and drilling processes that had already caused a delay of 16 hours, physically segregated and reliant on a specific operator. The process flow was improved by using planned machinery repositioning and job merging. After intervention analysis showed an improvement in availability (84.1 to 90.9 percent), quality rate (93.8 to 96.4 percent), and OEE (78.9 to 87.6 percent). Moreover, the intervention supported the 68-minute daily recovery, the daily increase of 15 units, and the yearly saving of ₹180000 in labor costs. The research confirms that VSM is a cost-effective lean tool that has the potential of causing massive improvements even in small and medium sized companies (SMEs). These results are very informative to industries that strive to improve throughput and eliminate waste without involving a large amount of capital.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".