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Record W2008929179 · doi:10.1504/ijism.2006.008337

Coordinating a three-level supply chain with price discounts, price dependent demand, and profit sharing

2005· article· en· W2008929179 on OpenAlexaff
Mohamad Y. Jaber, Ibrahim H. Osman, Alfred L. Guiffrida

Bibliographic record

VenueInternational Journal of Integrated Supply Management · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSupply chainProfit (economics)Profit sharingMicroeconomicsBusinessIndustrial organizationService managementSupply chain managementOrder (exchange)Economic order quantityEconomicsMarketingFinance

Abstract

fetched live from OpenAlex

This paper proposes a three-level (supplier-manufacturer-retailer) supply chain model with a profit sharing mechanism to maximise the supply chain profit. In this model, an all-unit price discounts scheme is used to coordinate the order quantities among the supply chain levels, and the demand at the retailer's end is assumed to be price dependent. To enhance the coordination in the supply chain, two profit-sharing scenarios are investigated. The semi-liberal scenario is based on increasing the quantity discount in order to generate more demands with which the most powerful player in the chain will get the highest fraction of additional profits. However, a strict mechanism is suggested to rectify the first scenario by dividing the coordination profits based on equal return on investments. Computational results are provided under different patterns of the players' order or set-up costs along the chain. Furthermore, the results show that the strict mechanism is the best mechanism for profit sharing among the players in the supply chain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.017
GPT teacher head0.232
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2005
Admission routes1
Has abstractyes

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