Optimal return policy and modular design for build‐to‐order products
Bibliographic record
Abstract
Abstract A build‐to‐order product gives the firm the opportunity to customize the product to the requirement of the customers. The firm will see an increase in demand, but face some operational difficulties. One of these is related to the return policy which is now a recognized tool to win customer orders. This is even more true for increasingly popular Internet sales where the opportunity to physically examine the product is absent. A tremendous advantage can be gained by the Internet firm if it could offer a return policy for BTO products. We propose the use of modularity in the product design as a solution to this problem. From manufacturer's point of view, following a policy of modularization and offering a generous return policy would increase revenue, but also increase the cost due to increased likelihood of return and increased cost of design. We develop a profit maximization model to jointly obtain optimal polices for return policy and modularity level in terms of certain market reaction parameters. We obtain a number of managerial guidelines for using marketing and operational strategy variables to influence those reaction parameters so as to obtain the maximum benefit from the market.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".