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Record W2954354629 · doi:10.29173/mocs107

Simulation Based Approach for the Industrialization of a Cabinet Manufacturing Facility

2019· article· en· W2954354629 on OpenAlexafffundvenueabout
R. A. S. Brown, Chelsea Ritter, Mohamed Al‐Hussein

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCabinet (room)Manufacturing engineeringProductivityProcess (computing)Computer scienceEngineeringOperations managementMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

High-end cabinet making is traditionally an artisan process that utilizes few manufacturing principles. Manufacturing lead time, labor hours required, and productivity can be improved by industrializing the process. This paper focuses on a case study of a high-end cabinet manufacturer in Edmonton AB, Canada and the proposed process and facility improvements. First, computer simulation using Simphony.NET and movement analysis of people/materials of the cabinet manufacturer’s current state of operations is conducted to establish a baseline. Next, suggested process and facility layout improvements and their anticipated results are quantified through future state simulation in order to aid management in making decisions for plant changes and to prove their effectiveness. These improvements include: application of lean principles, modification of their current production methods to reduce bottlenecks, and future state facility layout based on an optimized flow of people and materials.

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 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 score0.802
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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
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
Published2019
Admission routes4
Has abstractyes

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