A comprehensive survey into reverse logistics and Closed-Loop Supply Chain aspects to provide analyses and insights for implementation
Bibliographic record
Abstract
As a frequent part of the product life cycle in recent years, product returns rapidly fill up landfills and consequently cause serious environmental and social damage unless appropriately managed. Companies usually applied Reverse Logistics (RL) and Closed-Loop Supply Chain (CLSC) management to handle the returns. While examining RL and CLSC through social science and humanities perspectives provides valuable insights for decision-makers, this area is underexplored in the literature. Unlike the previous studies focusing on limited geographical scope and industry, this research explored global RL and CLSC activities in different industries. The results are also compared across developed and developing countries. Up-to-date data are collected through a novel questionnaire and analysed utilizing descriptive statistics, parametric and non-parametric statistical tests. The study’s results highlighted several key findings: a) direct feedback systems help reducing early returns, b) large companies benefit from expanding recovery options and return channels to handle more later returns, c) RL adoption improves both economic and environmental performances of companies worldwide, d) Economic and knowledge related barriers are the primary obstacles to RL adoption globally, and e) return uncertainty elements discourage top managers from implementing RL practices. The main theoretical implications include identifying factors such as company size and position in feedback system adoption, as well as the relationship between return channel expansion and both recovery practices and company size. Practically, effective RL adoption requires strategic management and financial support from governments and policy makers due to economic and knowledge-related barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".