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Record W346114403

PRINT PRODUCTION AUTOMATION: A CASE STUDY OF METROPOLITAN FINE PRINTERS

2015· article· en· W346114403 on OpenAlexaboutno aff
Velma Larteley Larkai

Bibliographic record

VenueSummit (Simon Fraser University) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)AutomationMetropolitan areaBusinessComputer scienceEngineeringGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The use of Management Information Systems (MIS) in improving organizational efficiency and productivity is one trend that has gained much traction in the print industry. MIS’ outstanding capabilities have demonstrated to be not just a better alternative to manual production workflow processes, but a necessity for print firms seeking to survive and thrive in an increasingly competitive sector.This report presents an investigation into how a Vancouver-based print company, Metropolitan Fine Printers (MET), achieves workflow automation through the use of MIS. It examines the introduction and implementation of MET’s first MIS, Hagen, and discusses the system’s capabilities, revealing the deficiencies that called for an upgrade of Hagen and its integration with other models, to give the new MIS system, Monarch, the potential to automate the workflow process of the company. The report then provides an assessment of the impact of Monarch on the company’s operations and presents the level of MIS integration within the company by examining work tasks completed by employees through the use of the system. In addition, it analyzes employee’s reaction and experience with Monarch and outlines the system’s weaknesses. The report concludes by presenting recommendations and strategies that MET could consider for enhancing its operations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.023
GPT teacher head0.219
Teacher spread0.196 · 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 designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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