Remanufacturing with innovative features: A strategic analysis
Bibliographic record
Abstract
In this study, we investigate the best remanufacturing strategy for the original equipment manufacturer (OEM) and independent remanufacturer (IR) in an innovative industry where the consumer valuation of the products increases with the level of innovation, and we characterize how the best strategy changes with the identity of the remanufacturer. Our work differs from existing articles that investigate the remanufacturing strategy in the presence of quality decisions, by actively including the innovative features in the remanufactured products, as opposed to passively carrying over product quality to the remanufactured products. We consider three remanufacturing strategies: (i) not remanufacturing, (ii) remanufacturing without adding innovative features, and (iii) remanufacturing adding innovative features (upgrading). To analyze the problem, we create a single-period model where the OEM determines the level of innovation and the quantity of new products, in both competitive settings, and either the OEM or IR determines the remanufacturing quantity depending on the competitive setting. We investigate how the firms’ environmental impact and the consumer surplus are affected by the competition and the remanufacturing strategy.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".