Cost‐raising internalization in supply chain design
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.
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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 | 0.003 | 0.009 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".