RISK REDUCTION OF THE SUPPLY CHAIN THROUGH POOLING LOSSES IN CASE OF BANKRUPTCY OF SUPPLIERS USING THE BLACK-SCHOLES-MERTON PRICING MODEL
Bibliographic record
Abstract
In recession times, slower demand, shrunk liquidity, and increasing pressure on cost can lead to bankruptcy of suppliers. The risks due to supplier bankruptcy include (a) losses due to supply chain disruption, (b) delayed or stopped finished goods shipments, (c) difficulty in finding cost-effective alternate suppliers and sourcing contracts, (d) emergency procurements, (e) loss of reputation and market share loss, etc. Bankruptcy models can be used to estimate the probability that a supplier may go bankruptcy, and a level of probability can be established that triggers the risks. This paper uses the Black-Scholes-Merton option pricing model for estimating the probability of bankruptcy of supplier by extracting and examining the riskiness in stock market price of supplier. The paper uses the pooling arrangements among companies that source from multiple suppliers as a way to reduce the risk due to supplier bankruptcy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".