MétaCan
Menu
Back to cohort
Record W3022769284 · doi:10.46254/j.ieom.20190104

New Framework to Optimize Leagile Supply Chain Design

2019· article· en· W3022769284 on OpenAlexaff
Dang Nguyen, Thiên My Dao

Bibliographic record

VenueInternational Journal of Industrial Engineering and Operations Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSupply chainComputer scienceProduct (mathematics)Product designConfiguration designProcess managementRisk analysis (engineering)EngineeringBusiness

Abstract

fetched live from OpenAlex

The literature of Leagile supply chain (LASC) is lacking of the concurrence between supply chain (SC) design and product design, and missing the placement of decoupling point (DP) in the SC design. Therefore, the paper aims at presenting a novel framework to optimise LASC design while fulfilling the aforementioned gaps. The first step utilises Lean tools to identify the optimal architecture of product families through the so-called Leagile bill-of-material in product design. This phrase intends to reduce the storage keeping unit of components (leaner) while increasing their combining ability in a wider range of new products (more agile). Meanwhile, the second stage outlines the preliminary configuration of the future supply chain and transforms it into the Lean system. Next, the supplier network of this chain is matched with the product structure. The last step formulates the issue in one mathematical model to define the optimal LASC’s configuration, which includes positioning the best DP in various delivery lead time. In discussing the obtained solutions, the article complements to the theoretical basis by examining the locations of DP corresponding to the product’s complexity. The whole framework is illustrated by one specific example and solved by Priority Generic Algorithm Meta-Heuristic, programed with MATLAB.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.606

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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Industrial Engineering and Operations ManagementSame topicQuality and Supply ManagementFrench-language works237,207