Quality investment, inspection policy, and pricing decisions in a decentralized supply chain
Bibliographic record
Abstract
This paper studies the interaction between two key quality management decisions—input conformance quality and inspection policy—and related wholesale and retail prices in a two echelon supply chain. Market demand depends on the retail price as well as the end‐product conformance quality, which itself depends on the input quality and the inspection scheme. Consistent with previous empirical findings in the literature, we show that an increase in quality does not always result in higher prices for consumers due to the cost‐lowering effect of better quality. We also show that a lower input quality may still result in higher end‐product quality because of how it might incentivize more and/or better inspection. Any interaction between input quality and inspection policy becomes more pronounced in the decentralized system due to incentive asymmetry between the channel partners. This makes the adoption of a full‐inspection policy more likely there compared to an integrated system. Indeed, while vertical competition due to decentralization results in higher prices for customers, it can also result in better quality of end products. Another interesting finding in the decentralized setting is that, somewhat counterintuitively, a player may indeed opt to bear a higher share of the penalty for defective products sold to consumers resulting in higher profits for the player.
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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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".