Dynamic Decision-Making in a Decentralized Price-Setting Supply Chain
Bibliographic record
Abstract
In this paper, we study a decentralized supply chain in which the manufacturer sells a short lifecycle product using wholesale price (only) contracts to a price-setting retailer, who in turn sells it to the market. Our two-period framework captures the salient features of this product: Price-sensitive stochastic retail demand; non-stationary demand and cost parameters; correlated demands which enable updating of demand characteristics; and limited, but possibly more than one, pricing, replenishment and wholesale pricing (contracting) opportunities. First, we develop a benchmark model where the integrated chain can price and order at the beginning of each period. We then model four decentralized decision-making paradigms with increasing degrees of decision flexibilities: Neither pricing nor ordering recourse; only pricing recourse; pricing and ordering recourses; and finally, pricing, ordering and contracting recourses. A novel transformation technique allows us to analytically characterize all the five models, and reduce the complex profit maximization problems in each case to a mere one-dimensional search. Subsequently, based on a numerical study, we systemat-ically compare the values and behaviors of the optimal decisions for the five models. In addition, we offer managerial insights as to how optimal decisions behave temporally and how they are affected by system characteristics like price elasticity, demand correlation and demand uncertainty. A more detailed investigation comparing the optimal profits for the five models allows us to identify the values of pricing, ordering and contracting flexibilities from the viewpoint of the two channel part-ners. Our analysis generates managerial suggestions as to which decision-making paradigm might be most suitable for the chain depending on the business environment and on the lifecycle phase of the product.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".