Determining the Influencing Variables in Sustainable Production Management for Carpet Factories in Iran: Sustainable Carpet Production System
Bibliographic record
Abstract
Objective: The purpose of this article is to define, categorize and prioritize the influencing variables in the process of carpet production to implement sustainable production management regarding controlling the influencing variables. Methodology: For this research, after reviewing the literature, sustainable production variables were extracted from previous studies. Then, the variables and their classifications were examined through interviews with experts using the Delphi method. Afterward, by using the detailed structural modeling of ISM (Interpretive Structural Modeling), the contextual relationships between the variables were determined. Finally, the influencing variables and their power of influence were evaluated via MICMAC analysis. This model has been implemented as a case study in Kashan carpet factories in Iran. Findings: The results showed that the most influential variables in sustainable production for carpet factories are training staff and managers, which have the greatest impact on the sustainability of carpet factories. The influences of other variables are measured and presented in the results tables, which can help managers with decision making. Originally: The innovative aspect of this article was the classification and prioritization of influential variables related to the sustainability of carpet factories in Iran, which can help managers in the decision-making process related to sustainable production systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".