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Record W2104289244 · doi:10.1080/07408170208928929

The value of information used in inventory control of a make-to-order inventory-production system

2002· article· en· W2104289244 on OpenAlexaff
Qi‐Ming He, Elizabeth Jewkes, John A. Buzacott

Bibliographic record

VenueIIE Transactions · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsYork UniversityUniversity of WaterlooDalhousie University
Fundersnot available
KeywordsInventory controlProduction (economics)Value (mathematics)Order (exchange)Economic order quantityInventory valuationPerpetual inventoryControl (management)Operations researchOperations managementInventory theoryComputer scienceBusinessMathematicsStatisticsEngineeringEconomicsMicroeconomicsMarketingSupply chainArtificial intelligence

Abstract

fetched live from OpenAlex

This paper studies a make-to-order inventory-production system consisting of a warehouse and a workshop. The concept of information level as the detail available on the number of unfilled demands at the workshop is introduced. The focal point is the value of the information used in inventory control in the warehouse. Dynamic programming is used to develop an algorithm for computing the optimal replenishment policy and the average total inventory cost per product. Numerical analysis is carried out and the results show that information used in inventory control can reduce the total inventory cost significantly. It is shown that the classical (Q, R) policy may not perform well if information about the number of demands is partially or fully available.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.201
Teacher spread0.183 · 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

Citations38
Published2002
Admission routes1
Has abstractyes

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