Cost‐raising internalization in supply chain design
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We investigate the supply chain design decisions faced by a focal retailer that participates in a two‐tier supply chain with another competing retailer and a common upstream supplier. The operations literature has traditionally approached supply chain network design with the objective of minimizing the overall costs from a centralized standpoint. We study the strategic consequences of the focal retailer's supply chain design decisions, and demonstrate how pursuing “cost‐raising” internalization could be beneficial. We build a stylized game‐theoretic model that captures the cost implications of the focal retailer's internalization decisions, and analyze the impact of these decisions on the equilibrium outcome. Under various assumptions on the nature of retail competition and supply chain contracts, we obtain simple and intuitive sufficient conditions for the profitability of cost‐raising internalization. Moreover, we study how the intensity of horizontal and vertical competition, respectively, may influence the focal retailer's supply chain decisions. Finally, we examine the impact of cost‐raising internalization on consumer surplus and channel performance, and find that welfare may increase under certain conditions. Our results challenge the presumption that supply chains always benefit from lowering their costs and highlight the fact that strategic considerations can sometimes lead a firm to make cost‐raising decisions within its supply chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it