Financing Disruptive Suppliers: Payment Advance, Timeline, and Discount Rate
Bibliographic record
Abstract
This study considers a dyadic supply chain in which a large and creditworthy buyer procures a product from a capital‐constrained supplier subject to disruption risk. In facilitating the production, the buyer may offer direct financing to the supplier by way of advance payment (AP). Concurrently, the buyer may also set a tailored discount rate (TR) that applies to the AP and an extended payment timeline (PE) for the balance due. We analyze the value and interplay of these elements by comparing optimal contractual terms with different AP, PE, and TR potentials. In general, AP applies to more reliable suppliers, and the coverage could be broadened by the inclusion of PE and TR. Specifically, when the advance discount rate is regulated within a certain limit, the buyer should offer TR without PE to the most reliable suppliers, vs. the floor discount rate with PE to those posing higher risk. Although the buyer normally benefits from practicing PE, the supplier benefits from it only when the risk level is relatively high and suffers from it when the risk level is relatively low; these effects persist although they are weakened in the presence of TR. Overall, PE and TR focus on different risk spectrums and are strategic substitutes for each other. The buyer can thereby retain its maximum payoff by properly configuring PE or TR when the other is under strict regulation. These insights offer strategic guidance for buyers to engineer business cash flows with respect to the risk level of their suppliers and the external regulation environment.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".