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Record W3022992088 · doi:10.46254/j.ieom.20190203

A Combined VSM and Kaizen Approach for Sustainable Continuous Process Improvement

2019· article· en· W3022992088 on OpenAlexaff
Sadaf Zahoor, Walid Abdul‐Kader, Hamza Ijaz, Atif Qayyum Khan, Zeeshan Saeed, Shoaib Muzaffar

Bibliographic record

VenueInternational Journal of Industrial Engineering and Operations Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsKaizenValue stream mappingManufacturing engineeringOverall equipment effectivenessLean manufacturingProcess (computing)DowntimeLean laboratoryEngineeringComputer scienceProcess engineeringReliability engineeringProduction (economics)

Abstract

fetched live from OpenAlex

The issues related to setup downtime, raw material waste, and the quality defects are inevitable in the flexographic printing business. To enable sustainable continuous process improvements within the printing process, lean manufacturing methodologies, such as Value Stream Mapping, (VSM), can be a competitive management approach. Therefore, this study explores how the systematic application of VSM in a flexographic printing process can foster further the process improvement when combined with other lean activities, such as, 5S, single minute exchange of die (SMED), and kaizen etc. To assess the contribution of this lean approach, the Overall Equipment Effectiveness, (OEE), and manufacturing costs are taken as performance metrics. The results demonstrate that when integrated with 5-why root cause analysis and kaizen, VSM improved OEE by 24.31% and reduced manufacturing costs from US$0.762 million to US$0.6 million. Hence, the significance of the proposed combined lean approach for continuous improvement is reached.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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