Optimal Outsourcing Strategies When Capacity Is Limited
Bibliographic record
Abstract
ABSTRACT Outsourcing the production of selected components to competitors is becoming more common among original brand manufacturers (OBMs); however, OBMs’ increased attention to outsourcing and the growing demand in many markets can result in capacity allocation conflicts for the contract manufacturers. In this study, we consider a scenario in which the OBM decides whether to outsource to a third‐party supplier or to a competitive contract manufacturer (CCM) who has the option of producing a competing product and also has limited capacity. This setting consists of two levels of competition: competition in the component market between the CCM and the spot market, and competition in the final‐product market between the OBM and the CCM. The CCM first chooses the wholesale price and decides whether or not to sell a competing product to the customers. Next, the OBM decides the proportion of its component demand to outsource to the CCM, and then firms set the retail prices. We are interested in investigating the impacts of the CCM's capacity and the impacts of these two levels of competition. We show that the OBM might multisource its component demand only when competition in the final‐product market is intense. We also find that when the CCM's capacity increases, demand may decrease, while the retail price may increase. Moreover, the CCM can be worse off from having more capacity, even when the CCM's capacity is available for free. Our results also show that demand may increase when competition in the final‐product market becomes more intense. Finally, we find that the value of having a third‐party supplier to produce the component decreases amid the intensity of competition in the final‐product market.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".