Benchmarking Sustainability Performance of Suppliers Using ISO 14001 and Rough Set QFD-Based Approach
Bibliographic record
Abstract
Supply chain management plays an important role in design, development, manufacturing, etc. and has key impact on company's overall environmental performance. Recently, green supply chain management has gained great interest from researchers and practitioners. Consideration has been given to consider environmental factors in entire supply chain starting from procurement, production, transportation, consumption, and post-disposal of products to make the whole product life cycle green. And those companies implementing ISO 14001 are controlling and minimizing risks not only internally but also externally with their suppliers. In this chapter, the authors are benchmarking sustainability performance of suppliers using ISO 14001 and rough set QFD. For this objective, firstly they identify the requirements for green supply chain planning on the basis of ISO 14001. Then, they evaluate the suppliers on the basis of these requirements using a QFD-based approach. To handle the uncertainties arising due to lack of or limited data, rough set theory is used. The results show that the proposed approach can effectively handle imprecise information and facilitates selection of green supply chain initiatives in a structured way.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".