The Coordinator Role of Trade Credit Contract for Coordinating Integrated Pricing and Periodic Review Inventory Decisions With Stochastic Demand
Bibliographic record
Abstract
The purpose of this study is to analytically coordinate joint pricing and periodic review ordering choices for a supply chain (SC) facing stochastic price-dependent demand. In this paper, first, the optimal inventory and pricing strategies made by SC members are obtained under decentralized and centralized models. Thereafter, employing a trade credit contract, it proposes to provide a win–win coordination for the investigated SC. Sensitivity analyses are provided both analytically and numerically which indicate the significance of trade credit contracts in improving both customer service level and customer satisfaction. The results indicate that using trade credit contracts, the coordination of joint pricing and periodic review ordering choices is achievable. Moreover, the proposed trade credit contract has a unique feature in achieving more economic benefit compared to the centralized model under some circumstances. In addition, sensitivity analyses reveal that the coordination of integrated periodic review ordering and pricing choices is beneficial, especially for items with high price sensitivity and high demand uncertainty. Our findings also show the applicability of the trade credit contract for an SC facing highly uncertain demand.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".