Transparency for Multi-Tier Sustainable Supply Chain Management: A Case Study of a Multi-tier Transparency Approach for SSCM in the Automotive Industry
Bibliographic record
Abstract
Sustainability in supply chain management (SSCM) has become established in both academia and increasingly in practice. As stakeholders continue to require focal companies (FCs) to take more responsibility for their entire supply chains (SCs), this has led to the development of multi-tier SSCM (MT-SSCM). Much extant research has focused on simple supply chains from certain industries. Recently, a comprehensive traceability for sustainability (TfS) framework has been proposed, which outlines how companies could achieve MT-SSCM through traceability. Our research builds on this and responds to calls for cases from the automotive industry by abductively analysing a multi-tier supply chain (MT-SC) transparency case study. This research analyses a raw material SC that is particularly renowned for sustainability problems—the cobalt supply chain for electric vehicles—and finds that the extant literature has oversimplified the operationalisation of transparency in MT-SSCM. We compare the supply chain maps of the MT-SC before and after an auditing and mapping project to demonstrate the transparency achieved. Our findings identify challenges to the operationalisation of SC transparency and we outline how FCs might set to increase MT-SC transparency for sustainability.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".