Simulation-based value stream map for manual steel fabrication workstations
Bibliographic record
Abstract
Industrial steel fabrication encounters numerous difficulties in having efficient steel production. These difficulties are usually caused by the presence of bottlenecks that are not easily identified using traditional methods. Value Stream Map (VSM) is a lean tool that can be used to identify process inefficiencies and plan to minimize non-value-added activities. However, traditional VSM is not designed to be applied in a non-repetitive work environment especially when workers are highly involved in the process such as in manual steel fabrication processes. Therefore, the typical VSM requires modification to achieve the desired outcomes. Hence, the goal of the current study is to produce a current state VSM and integrate it with a simulation model to plan for an optimized future state. The scope of this study is limited to the fitting and welding workstations since they represent the typical manual processes in the steel fabrication industry. The outcomes of this study will provide the ability to identify waste and improvement opportunities. Also, it will allow for precise quantification of the improvement gain and time savings. Decision-makers in the steel industry will have accurate information about the processes in fabrication plants. Also, they will be able to make evidence-based decisions that will ensure reduced waste and cost for steel operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".