Inventory, Speculation, and Sourcing Strategies in the Presence of Online Exchanges
Bibliographic record
Abstract
We study how online business-to-business (B2B) exchanges affect buyer-supplier relationships where an exchange takes the role of a secondary market in which buyers (of the initial product) can trade excess inventory to address supply and demand imbalances. Over the last several years, B2B exchanges have attempted to provide supply for storable industrial goods with some degree of design specification (as opposed to undifferentiated commodities). Through this research, we elucidate some aspects of how speculative online exchanges with a small number of participants might behave and the impact they will have on the use of long-term contracts for supply. By endogenizing the evolution of spot prices in response to buyers’ and their supplier’s actions, we produce price fluctuations that exhibit significant autocorrelation in such markets. We show that participating buyers accrue network benefits as the number of participating firms increases through the inventory-pooling effects, resulting in reduced costs for them. However, a supplier acting strategically will counteract such benefits by restricting availability of goods to the spot market, sacrificing short-term spot-market revenue for long-term contract volume.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".