Sourcing Strategy of Original Equipment Manufacturer with Quality Competition
Bibliographic record
Abstract
ABSTRACT We develop game‐theoretic models to study the sourcing strategy of an original equipment manufacturer (OEM) in the presence of a competing contract manufacturer (CCM). The OEM sells the product in a high‐quality brand and has three strategy options: producing the product in house or outsourcing production to either a CCM or a noncompeting contract manufacturer (NCM). At the same time, the CCM provides its own product in a low‐quality brand. We show that when the product qualities are exogenous, the OEM's sourcing strategy decision depends on the OEM brand's efficiency in producing and selling the product (the difference between quality and production cost), relative to that of the CCM brand. Interestingly, the OEM may prefer outsourcing to a CCM to soften price competition, even though this will mean a higher wholesale price; the CCM also prefers to produce for the OEM, even though its total demand may decrease. When the product qualities are endogenous, however, with quality competition, the OEM prefers either insourcing or outsourcing to an NCM to increase quality differentiation, depending on its cost disadvantage in producing the high‐quality 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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".