Managing Product-Reusability Under Supply Disruptions
Bibliographic record
Abstract
We analyze product reusability, executed through refurbishment, amid supply disruptions. Consumers trade in used units, which can later be refurbished and sold. Using a three-period model, we determine the optimal reusability level, trade-in and refurbishment policies, trade-in fee, and the prices of new and refurbished units. Our analysis provides a useful framework to understand the interaction between a firm's choice of product reusability and the possibility of supply disruptions. First, we establish a threshold refurbishment policy: the firm refurbishes more as reusability increases but avoids refurbishment at low reusability. When both consumer valuation of used units and supply disruption probability are high, the firm builds a safety-stock of traded-in units, which it refrains from refurbishing when there is no supply disruption, unless the product reusability level is sufficiently high. Second, we find that it benefits to increase product reusability as the supply disruption risk increases until a certain threshold. Beyond this threshold, it is advantageous for the firm to reduce reusability and save on design costs to be profitable, contrary to popular belief. Our numerical examples reveal that the firm reduces reusability when production cost is high due to narrow margins. Finally, we demonstrate that the firm shares the benefits of higher product reusability with its consumers through higher trade-in fees and lower refurbished unit prices. This results in a “Pareto-efficient” win for the firm, its trade-in customers, and purchasers of refurbished units. Thus, our analysis offers insights for product designers on how supply disruption influences reusability choices in products.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".