Providing a Green Value Stream Map to Improve Production Performance
Bibliographic record
Abstract
Today, the cement industry has gone through a growing trend. Achieving the country's economic development, social development and cultural development goals is essential. However, in line with these benefits, the environmental damage caused by cement factories is inevitable. In the present research, which was carried out to reduce environmental losses, value flow mapping and simulation by Arena software were used in two stages. It was determined in the first stage using the current simulated situation and the waste and environmental pollution created. Then by redrawing the future value flow map and using experts' opinions, the amount of reduced pollution caused by some measures was estimated. Then, in the second stage a new simulation was done to evaluate the reduced environmental pollution. The results of this research showed that by using the methods mentioned above in the primary production line process of the Cement Company, about 30% of waste and pollutions were reduced. Received: 25 April 2023 | Revised: 25 June 2023 | Accepted: 27 July 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Somaieh Alavi: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing - original draft, Supervision, Project administration. Parisa Siamaki: Software, Formal analysis, Investigation, Data curation, Writing - review & editing, Visualization. Seyedmehdi Mirmohammadsadeghi: Conceptualization, Methodology, Validation, Investigation, Writing - original draft, Supervision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".