Forecast Model for Return Quality in Reverse Logistics Networks
Bibliographic record
Abstract
Giving rise to the field of reverse logistics are the governmental legislations mandating used electronics take-backs and sustainable recovery, which often burden manufacturers with the challenge of high implementation costs but no guaranteed profitability. One way to tackle this challenge is to demystify the multi-faceted uncertainties of product returns, namely timing, quantity and quality, that currently inhibit optimal design and operations of reverse logistics networks (RLN). In recognition of the limitations particularly caused by uncertainty of returns’ quality in the strategic, tactical and operational planning of the RLN, this research seeks to develop a forecast model for the prediction of the returns’ quality of future electronics returns. The proposed forecast model comprehensively incorporates three major factors that affect quality decisions which are usage, technological age and remaining economic value of expected product returns to predict its quality grade. While technological age and economic trends can readily be established, the main complexity lies in modeling of usage-dependent reliability distribution of returned electronics. The novelty of the proposed forecast model lies in deducing usage distributions through segmentation of the consumer base by socioeconomic factors such as age, income, educational status and location. These usage distributions are then used to estimate remaining useful life of returned products and their components, the associated repair costs and the subsequent profitability of reprocessing based on economic value in the market. This research develops analytical models of expected return quality based on empirical usage distributions and pricing trends. The analytical models are then applied in Monte Carlo simulations to forecast expected returns’ quality from different regions, including large and small population centers, in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".