Coping with an unreliable supplier: An option contract with a backup supplier
Bibliographic record
Abstract
Abstract This study considers a channel with a retailer who satisfies demand by ordering from two suppliers: a low‐cost primary supplier whose production is subject to failure and an expensive reliable backup supplier. We develop a call option contract and evaluate the preference of each party through two negotiable scenarios: (i) the backup supplier only decides on option price and (ii) the backup supplier determines both option and exercise prices. We find that higher authority and flexibility of the backup supplier in making decisions in the second scenario results in a lower option price, yet more possibility of achieving channel coordination compared with the first scenario. While the backup supplier prefers the second scenario, the retailer would rather adopt the other one. The results also shed light on when/how the call option contract can be beneficial for each participant. Specifically, when the primary supplier is unable to supply the whole demand, the proposed option contract can be a win‐win Pareto‐improving profit‐sharing mechanism only if the likelihood of failure in the primary supplier's production is sufficiently low. Otherwise, the backup supplier may be a detrimental option for the retailer.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".