Optimal Prices and Trade-in Rebates for Durable, Remanufacturable Products
Why this work is in the frame
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Bibliographic record
Abstract
Most durable products have two distinct types of customers: first-time buyers and customers who already own the product, but are willing to replace it with a new one or purchase a second one. Firms usually adopt a price-discrimination policy by offering a trade-in rebate only to the replacement customers to hasten their purchase decisions. Any return flow of products induced by trade-in rebates has the potential to generate revenues through remanufacturing operations. In this paper, we study the optimal pricing/trade-in strategies for such durable, remanufacturable products. We focus on the scenario where the replacement customers are only interested in trade-ins. In this setting, we study three pricing schemes: (i) uniform price for all customers, (ii) ageindependent price differentiation between new and replacement customers (i.e., constant rebate for replacement customers), and (iii) age-dependent price differentiation between new and replacement customers (i.e., agedependent rebates for replacement customers). We characterize the roles that the durability of the product, the extent of return revenues, the age profile of existing products in the market, and the relative size of the two customer segments play in shaping the optimal prices and the amount of trade-in rebates offered. Throughout the paper we highlight the operational decisions that might influence the above factors, and we support our findings with real-life practices. In an extensive numerical study, we compare the profit potential of different pricing schemes and quantify the reward (penalty) associated with taking into account (ignoring) customer segmentation, the price-discrimination option, return revenues, and the age profile of existing products. On the basis of these results, we are able to identify the most favorable pricing strategy for the firm when faced with a particular market condition and discuss implications on the life-cycle pricing of durable, remanufacturable products.
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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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it