Dynamic Supplier Contracts Under Asymmetric Inventory Information
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we examine a supply chain in which a single supplier sells to a downstream retailer. We consider a multiperiod model with the following sequence of events. In period t the supplier offers a contract to the retailer, and the retailer makes her purchasing decision in anticipation of the random demand. The demand then unravels, and the retailer carries over any excess inventory to the next period (unmet demand is lost). In period t+1 the supplier designs a new contract based on his belief of the retailer's inventory, and the game is played dynamically. We assume that short-term contracts are used, i.e., the contracting is dynamically conducted at the beginning of each period. We also assume that the retailer's inventory before ordering is not observed by the supplier. This setting describes scenarios in which the downstream retailer does not share inventory/sales information with the supplier. For instance, it captures the phenomenon of retailers distorting past sales information to secure better contracting terms from their suppliers. We cast our problem as a dynamic adverse-selection problem and show that, given relatively high production and holding costs, the optimal contract can take the form of a batch-order contract, which minimizes the retailer's information advantage. We then analyze the performance of this type of contract with respect to some useful benchmarks and quantify the value of prudent contract design and the value of inventory information to the supply chain. Markovian adverse-selection models, in which the state and action in a period affect the state in the subsequent period, are recognized as theoretically challenging and are relatively less understood. We take a nontrivial step towards a better understanding of such models under short-term contracting.
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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 | 0.002 | 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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