Managing build‐to‐order short life‐cycle products: benefits of pre‐season price incentives with standardization
Bibliographic record
Abstract
Abstract In this paper we consider a firm that can produce standardized products (SP) or customized build‐to‐order products (BOP) with short life‐cycles. We develop a stochastic dynamic programming model to study the effect of offering price incentives to those customers who are willing to commit themselves to purchasing the cheaper‐to‐produce standardized product. The market’s reaction to price incentives is captured by modeling the probabilistic behavior of two exclusive groups of customers who may be “price‐sensitive” or “price‐insensitive” and who are willing to commit themselves. We develop an analytic expression for the distribution of the demand for the BOP conditional on the actual number of committed demands for SP following the price‐incentive announcement. We show that price incentives expand the total expected demand while reducing demand uncertainty and we present conditions under which it is optimal to offer a price incentive. Sequentially made price incentive and capacity decisions for the BOP that maximize expected profit are obtained by solving the dynamic programming problem. We provide a comparative statics analysis and examine the effect of varying parameter values on the optimal price‐incentive discount rate. We also describe a numerical study and present a sensitivity analysis of the profit improvement obtained after offering price incentives. We show that for low purchase probability, large number of customers in the price‐sensitive group and high profit margin price incentives can substantially improve the expected profit.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".