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Record W1981277366 · doi:10.1504/ijlsm.2012.046705

Composite sourcing policy considering raw-material consumption

2012· article· en· W1981277366 on OpenAlex
Taebok Kim, Suresh Kumar Goyal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Logistics Systems and Management · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsRaw materialStrategic sourcingProfit (economics)SizingSupply chainConsumption (sociology)BusinessProduction (economics)Computer scienceEnvironmental economicsScheduling (production processes)Industrial organizationFinished goodOperations researchOperations managementMicroeconomicsEconomicsStrategic planningMarketingMathematics

Abstract

fetched live from OpenAlex

In this paper, we study a composite sourcing policy considering raw-material consumption so as to maximise the expected total profit while considering mixed strategy for sourcing policy with the lot-sizing issue for multiple products. It is proved that the optimal mixed strategy considering the consumption of raw materials has a property for strong local maximum. Using this property, we propose the economical sourcing policy while considering the relevant cost for raw material management. We analyse and illustrate the behaviours of economical sourcing policy by numerical examples. For future research, it is necessary to take into account both quality issue of supplied items with different production modes and scheduling issue between intermediate inputs and finished goods along supply chain stages.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.042
GPT teacher head0.273
Teacher spread0.231 · 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