Developing a Framework on Designing a Sustainable Supply Chain by Integrating Input-Output Analysis and DEMATEL Method: A Case Study on Textile Industry in Indonesia
Bibliographic record
Abstract
The textile industry is one of the manufacturing industries experiencing rapid growth.This follows the magnitude of the impact of the textile industry supply chain from an economic and environmental perspective.Thus, analyzing the supply chain structure at the macro level is essential to understand the supply chain better.This study develops an approach that uses Input Output (IO) data taken from the World Input-Output Database (WIOD) to measure environmental impacts at the economic sector level.This study aims to design the textile industry's supply chain structure and identify the method used, which combines IO analysis and DEMATEL (Decision-Making Trial and Evaluation Laboratory).The novelty of this research is that it proposes a method to calculate the expected interaction of CO2 emission within the supply chain.The results show the three-tier supply chain structure of textile industries in Indonesia.The leading suppliers of textile industries are the Manufacture of chemicals and chemical products (r11), wholesale trade (r29), and Crop and animal production (r1).Meanwhile, the sectors most polluting in the supply chain are electricity and gas (r24), the Manufacture of chemicals and chemical products (r11), and crop and animal production (r1).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".