Unpacking the Challenges of Supply Chain Transparency and Traceability: Perspectives from Industry Practitioners
Bibliographic record
Abstract
Supply chain transparency and traceability have become increasingly important in today's globalized and interconnected business environment. This qualitative research explores the challenges and perspectives of industry practitioners regarding supply chain transparency and traceability. Drawing on semi-structured interviews with a diverse group of supply chain professionals, the study identifies key themes including difficulties in achieving visibility beyond immediate suppliers, data fragmentation and inconsistency, barriers to technology adoption, regulatory compliance, cultural and organizational factors, impacts on supplier relationships, and the influence of the COVID-19 pandemic and sustainability goals. The findings highlight the complex nature of these challenges and the need for collaborative, multi-stakeholder approaches to address them effectively. Despite the challenges, the study also reveals the potential benefits of transparency and traceability for enhancing supply chain resilience, sustainability, and stakeholder trust. Moving forward, companies must leverage technological innovations, establish clear communication channels with suppliers, navigate regulatory complexities, and foster a culture of transparency and accountability within their organizations. Future research should focus on practical strategies for implementing transparency and traceability initiatives, as well as evaluating their impact on supply chain performance and sustainability outcomes.
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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 | 0.083 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| 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".