Partial centralization in a durable‐good supply chain
Bibliographic record
Abstract
There has been extensive research on the strategic choice between supply chain centralization and decentralization. However, most research assumes complete centralization or complete decentralization but omits the commonly adopted supply chain structure of partial centralization. With partial centralization, a firm owns a portion, but not all, of its partner. To help fill this research gap, in this paper, we make a major contribution by explicitly analyzing partial centralization in a supply chain where a durable‐good manufacturer owns a portion of its downstream retailer. We start with a two‐period model and derive analytical equilibrium outcomes of the supply chain and its members under complete centralization, complete decentralization, and partial centralization. First, our analysis reveals that partial centralization with an appropriate portion of ownership can yield the desirable product sales pattern over periods and alleviate the time‐inconsistency problem in selling durable goods. As a result, partial centralization can become the equilibrium structure for a durable‐good supply chain. Second, the manufacturer's optimal ownership level in the retailer decreases in the product durability and decision horizon length, implying that complete decentralization is more likely to be the supply chain structure in equilibrium for higher product durability and longer decision horizon. Third, our extended analyses demonstrate the robustness of the main results with backward partial centralization. Moreover, partial centralization outperforms conventional strategies such as two‐part tariffs that can coordinate a supply chain but not fully solve the time‐inconsistency problem associated with durable goods.
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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 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".