MétaCan
Menu
Back to cohort
Record W2144981152 · doi:10.5430/bmr.v3n1p54

Calculation of the Approaches to Cycle Service Level in Continuous Review Policy: A Tool for Corporate Entrepreneur

2014· review· en· W2144981152 on OpenAlexvenueno aff
Sofía Estellés-Miguel, Manuel Cardós, José Miguel Albarracín Guillem, Marta Palmer Gato

Bibliographic record

VenueBusiness and Management Research · 2014
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
FundersUniversitat Politècnica de València
KeywordsEntrepreneurshipAnalogyDimension (graph theory)Boosting (machine learning)Computer scienceCompetitive advantageEconomicsService levelService (business)Order (exchange)Industrial organizationBusinessMarketingManagementMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents two new approximations to compute the Cycle Service Level (CSL) in a continuous review policy, as a tool for corporate entrepreneur. These approximations are not only for the backordering case but also for the lost sales one. In order to develop it we focus on transforming a form of periodic review policy in a model of continuous review policies. As a result, the analogy and the transformation proposed in this paper are different from Silver classical model. Due to huge complexity of the exact CSL calculus the aproximate methods are needed. The entrepreneurial dimension, based on internal reorganization and innovation, is an inherent, indispensable part of the discovery and creation of opportunities. Accordingly, this article contributes to the firms´ pursuit of competitive advantages by presenting methods of internal management for corporate entrepreneurship. Efficient stock-taking is a key issue for lowering production cost and boosting competitive advantages, and, by examining this area, this paper makes a significant contribution to the study of corporate entrepreneurship.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.496
GPT teacher head0.368
Teacher spread0.128 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueBusiness and Management ResearchSame topicFirm Innovation and GrowthFrench-language works237,207