Supply-Side Story: Risks, Guarantees, Competition, and Information Asymmetry
Bibliographic record
Abstract
The risk of supply disruption increases as firms seek to procure from cheaper, but unproven, suppliers. We model a supply chain consisting of a single buyer and two suppliers, both of which compete for the buyer's order and face risk of supply disruption. One supplier is comparatively more reliable but also more expensive, whereas the other one is less reliable but cheaper and faces higher risk of disruption. Moreover, the risk level of the unreliable supplier may be private information, and this lack of visibility increases the buyer's purchasing risk. In such settings, the unreliable supplier often provides a price and quantity (P&Q) guarantee to the buyer. Our objective is to study the underlying motivation for the guarantee offer and its effects on the competitive intensity and the performance of the chain partners. Our model also includes a spot market that can be utilized by any party to buy or to sell. The spot market price is random, partially depends on the available capacity of the two suppliers, and has a positive spread between buying and selling prices. We analytically characterize the equilibrium contracts for the two suppliers and the buyer's optimal procurement strategy. First, our analysis shows that P&Q guarantee allows the unreliable supplier to better compete against the more reliable one by providing supply assurance to the buyer. More importantly, when information asymmetry risk is high, use of a guarantee may enable the unreliable supplier to credibly signal her true risk, thereby improving visibility into the chain. This signal can also be used by the buyer to infer the expected spot market price. In spite of these benefits, a guarantee offer in an asymmetric setting may not always be desirable for the buyer. Rather, it can reduce competition between the suppliers, resulting in higher costs for the buyer. This paper was accepted by Martin Lariviere, operations management.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".