Closed‐loop supply chain activities and derived benefits in manufacturing SMEs
Bibliographic record
Abstract
Purpose Closing the loop at the end of products' useful life is earning increased attention from industry and academia. The recent or upcoming enactment of regulations regarding the management of end‐of‐life products is forcing manufacturers to consider strategies to increase the residual value of the products they make. Facilitating the residual value extraction process for end‐of‐life products is a challenging issue deserving investigation. This paper proposes to investigate this issue. Design/methodology/approach This paper analyzes empirical evidence from a sample of 205 environmentally responsive SMEs operating in the fabricated metal products and electric/electronic products industries. A coherent research model is developed which classifies the closed‐loop supply chain (CLSC) activities along two dimensions, the forward and reverse supply chains. Findings This first proposed taxonomy has been shown to be relevant for both sectors. The results also demonstrate that firms' abilities to implement CLSC environmental initiatives vary in their intensity and in their locus along the product value chain. Furthermore, benefits derived from these initiatives seem to vary according to the strategy favored by the firms. Originality/value This research is valuable for those firms interested in implementing CLSC strategies in a synergistic manner with their forward supply chain.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".