The Effect of Introducing Upgraded Remanufacturing Strategy on OEM’s Decision
Bibliographic record
Abstract
Although remanufacturing has great economic and environmental potential, internal cannibalization, and lack of consumer acceptance of remanufactured products prevent original equipment manufacturer (OEM) from realizing the full potential value through remanufacturing. Practices show that remanufactured products can realize their value by the donation, besides resale. Thus, this paper incorporates the donation of remanufactured products with government subsidy and presents an upgraded remanufacturing strategy to expand the demand for remanufactured products and weaken the internal cannibalization of remanufactured products. We respectively construct the two-period game model with and without upgraded remanufacturing and explore the effect of upgraded remanufacturing on production decision, economic and environmental benefits. The main conclusions are as follows. The donation subsidy is negatively related with the sale quantity of remanufactured products, but is positively related with the donation quantity of remanufactured products and the quantity of new products. The donation subsidy expands the demand for remanufactured products and weakens internal cannibalization of remanufactured products. Whether the upgraded remanufacturing strategy is profitable depends on the fixed cost of the remanufacturing. When consumers consider remanufactured products environmentally friendly, the government can realize an OEM’s win-win situation where the economic and environmental benefits get improved by adjusting the donation subsidy. Otherwise, introducing upgraded remanufacturing makes the environment worse. Comparatively speaking, a low-cost and environmentally friendly manufacturer is relatively easier to achieve the win-win situation through donation subsidy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".