Original equipment manufacturer with remanufacturing: Outsourcing strategy and organizational structure
Bibliographic record
Abstract
We consider an original equipment manufacturer (OEM) with two divisions: one manufacturing and selling new products, and the other remanufacturing used products for sale. The OEM can choose between centralizing or decentralizing its manufacturing and remanufacturing divisions. The OEM’s product contains a key component that cannot be reused in the remanufactured product. Each unit of the new/remanufactured product requires one unit of this new component. The OEM may either produce this component in house or outsource its production to a supplier. By developing game-theoretic models, we investigate the impact of the OEM’s internal organizational structure on its outsourcing decision, and the impact of the OEM’s outsourcing strategy on its choice of internal organizational structure. For a given internal organizational structure, we show that the levels of the supplier’s cost advantage and the cost saving from remanufacturing are the primary drivers in the OEM’s outsourcing decision. Moreover, the OEM’s internal organizational structure has significant implications for its choice of outsourcing strategy. Specifically, when the cost saving from remanufacturing is moderately low or sufficiently high, the decentralized OEM is more likely to choose outsourcing, while the centralized OEM is more likely to outsource when the cost saving from remanufacturing is moderate. For a given outsourcing strategy, we show that the OEM’s choice of internal organizational structure is significantly influenced by its outsourcing strategy. While the insourcing OEM always prefers a centralized structure, the outsourcing OEM may strategically decentralize its internal organizational structure, depending on the level of cost saving from remanufacturing and the degree of consumer acceptance of the remanufactured product.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".