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Record W2914707671 · doi:10.5555/3320516.3320897

Simulating an integrated revenue management approach in a production system with product substitution

2018· article· en· W2914707671 on OpenAlexaffabout
Maha Ben Ali, Sophie DrAmours, Jonathan Gaudreault, Marc-André Carle

Bibliographic record

VenueCorpus Université Laval (Université Laval) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsUniversité TÉLUQUniversité Laval
Fundersnot available
KeywordsContext (archaeology)RevenueRevenue managementConsumption (sociology)Product (mathematics)Production (economics)Substitution (logic)Demand managementComputer scienceNew product developmentBusinessIndustrial organizationOperations researchEconomicsMicroeconomicsMarketingEngineering

Abstract

fetched live from OpenAlex

Most revenue management publications dealing with substitutable products in a manufacturing context
\nhave focused on pricing issues. They consider that substitution is a customer’s decision which occurs
\nas a response to product price differences. In our study, substitution is considered as the firm’s policy.
\nWe focus on the extension of the revenue management to practical applications in manufacturing and we
\nare motivated by the Canadian softwood lumber case where product substitution is a common demand
\nfulfillment practice. We aim, first, to propose a generic consumption model integrating both capacity
\ncontrol and product substitution decisions and second, to evaluate, using a rolling horizon simulation,
\nthe performance of this integrated model in different settings compared to common demand fulfillment
\napproaches. In addition to practical implications, our study contributes to the existing demand fulfillment
\nliterature since we simulate different consumption models integrated with a Sales and Operations Planning
\nmodel.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.180
Teacher spread0.167 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2018
Admission routes2
Has abstractyes

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