Management Systems and Good Practices Related to the Sustainable Supply Chain Management
Bibliographic record
Abstract
The objective of this paper is to identify the good practices and management systems related to Sustainable Supply Chain Management SSCM. This study is qualitative with exploratory objective. By examining SSCM publications across journals and across time (from 1997 to 2011) in the three aspects of sustainability, there is a growing number from 2004/2005 onwards. The social aspect is rarely addressed in the literature, in contrast with environmental followed by the economic. The management systems reflect this finding as well as with the eight practices identified. There is no management system that addresses social responsibility. Only two practices address the triple bottom line aspects, while four address environmental and economic aspects, one deals with social and economic and one deals with environmental aspect. The two frameworks proposed by the authors are the contribution in this paper. The first relates the good practices adopted in SSCM to the three aspects of sustainability and their interactions. The second organizes the management systems in layers according to how they address the sustainability aspects. SSCM, as well as Sustainable Operations Management, present themselves as the most comprehensive management model. In SSCM new practices are used together with those found in the supply chain management.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".