An initiative to implement lean manufacturing using value stream mapping in a small company
Bibliographic record
Abstract
In recent years, Value Stream Mapping (VSM) has emerged as the preferred way to support and implement the lean approach. VSM is a helpful tool to identify the waste and improvement areas. VSM enables a company to see the entire process in both its current and desired future state, and develop the road map that prioritises the projects or tasks to bridge the gap between the current state and the future (lean) state. VSM is a visual illustration of the entire value stream (from customer order entry through purchasing, manufacturing and shipping of the finished product in a facility). This paper describes the implementation of VSM in a small manufacturing firm as a lean manufacturing improvement initiative. This involved mapping the activities of the firm, identifying opportunities for improvement and then undertaking with the firm an improvement programme. Current state map is prepared to describe the existing position and various problem areas. TAKT time calculations are carried out to set the pace of production. Future state map is prepared to show the proposed improvement action plans. The achievements of value stream implementation are reduction in lead time, cycle time and inventory level. It was found that even a small company can make significant improvements by adopting VSM.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".