OPTIMAL PROCUREMENT STRATEGY UNDER SUPPLY RISK
Bibliographic record
Abstract
With the rapid expansion of global business, newer suppliers with cheaper but possibly unreliable technologies have entered the marketplace to win orders from buyer firms by beating the price of their perfectly reliable (but expensive) competitors. We model the procurement problem as a Nash game where the buyer has to allocate its purchases between an expensive but reliable supplier, and a cheaper but unreliable supplier. The suppliers specify prices for different proportions of the order awarded to them. Our analysis shows that, when perfect information is available about the reliability level of the unreliable supplier, the Nash equilibrium is a sole-sourcing allocation and that the supplier selection decision depends on the reliability and cost differentials between the two suppliers. In addition, we model the case when the buyer and the reliable supplier have limited information about the reliability of the unreliable supplier. Even in such an asymmetric scenario, the buyer's equilibrium allocation is a sole-sourcing outcome, but depending on system conditions either a separating or a pooling equilibrium is possible. An interesting insight into the effect of information asymmetry is that it can result in higher or lower profits/costs for the channel partners (compared to the perfect information case). As such, the buyer may even benefit from information asymmetry regarding unreliable supplier due to its impact on the degree of competition between the two suppliers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".