A Trade Credit Model with Asymmetric Competing Retailers
Bibliographic record
Abstract
We study a supply chain of a manufacturer selling to two asymmetric retailers engaged in inventory (order quantity) competition in the presence of demand uncertainty and an exogenously given retail price. The effective demand of each retailer includes its primary demand and reallocated demand from its competitor. We model two salient features causing asymmetry: (i) the weak retailer is capital‐constrained and (ii) the bargaining power of the dominant retailer implies that it enjoys a lower wholesale price. The manufacturer offers trade credit to the weak, capital‐constrained retailer. We show that such trade credit can be used by the manufacturer as a strategic response to the bargaining power of its dominant retailer. Computational examples reveal that under inventory competition, the capital‐constrained retailer benefits from the trade credit, leaving the dominant retailer worse off. We show that demand substitution increases the profit of the dominant retailer and the manufacturer but, somewhat surprisingly, decreases the weak retailer's profit. When both bank and trade credit are available, we show conditions under which trade credit is preferred over bank credit by the manufacturer. Compared with a trade credit with an endogenous interest rate (and an exogenously given wholesale price), a trade credit with an endogenous wholesale price (and an exogenously given interest rate) is preferred by the manufacturer, but is only preferred by the system when the weak retailer's initial working capital is small.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".