Reverse Logistics: Design implications on the basis of product types sharing identical supply chain member motivations
Bibliographic record
Abstract
Reverse logistics plays a very critical role in the overall strategy of a business and hence need to be very effective in meeting its objectives.Studies have come up with various insights to optimize reverse logistics arrangements within a specific industry or a sector, but presently there is no study which provides an approach to share knowledge drawn out of reverse logistics arrangements, across dissimilar industries and sectors.Such a study is significant because the response to a reverse logistics arrangement is not uniform in an industry or sector in all the countries, due to different market maturity levels, dissimilar consumer behaviour, and the state of the economy itself.Therefore, the purpose of this paper is to provide a guide for logistics planners through which they can utilize the learning outcomes that emerge from dissimilar industries or sectors within the same economy also.The research findings show that the reverse logistics arrangements can be categorised into various types on the basis of origin and reason for return.It is shown that the products with dissimilar characteristics can be grouped together into six types depending on the common supply chain member interests.Further, the reverse logistics arrangements change from one type to another as a product moves across its life stages.It highlights an approach using which the knowledge drawn from a reverse logistics type in one sector/industry can be applied to the same type in another sector/industry, by focusing on the product types, whose return share similar supply chain member interests.Logistics network planners can apply the insights that have emerged from this analysis to effectively design reverse logistics channels.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".