Evidence map›Paper›PMID 35287321›Full record

ArticleHeliyon2022

Application of lean Six Sigma methodology using DMAIC approach for the improvement of bogie assembly process in the railcar industry.

Ilesanmi Daniyan, Adefemi Adeodu, Khumbulani Mpofu, Rendani Maladzhi, Mukondeleli Grace Kana-Kana Katumba

Abstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ilesanmi DaniyanDepartment of Industrial Engineering,Tshwane University of Technology, Pretoria 0001, South Africa.
Adefemi AdeoduDepartment of Mechanical Engineering, University of South Africa, Florida, South Africa.
Khumbulani MpofuDepartment of Industrial Engineering,Tshwane University of Technology, Pretoria 0001, South Africa.
Rendani MaladzhiDepartment of Mechanical Engineering, University of South Africa, Florida, South Africa.
Mukondeleli Grace Kana-Kana KatumbaDepartment of Industrial Engineering,Tshwane University of Technology, Pretoria 0001, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quest for waste reduction, quality improvement and operational efficiency during the assembly process of the railcar bogie necessitates this study. Using a case study approach, this study employs the Lean Six Sigma (LSS) approach for process improvement of the railcar bogie assembly process. Primary data relating to the assembly process such as the labour and material flow, up and down times were collected at every stage of the assembly operation. The improvement process of the assembly process featured the use of some Lean tools such as the Kaizen, Value Stream Mapping, Pareto chart, Single-Minute Exchange of Die (SMED) and 5S. The investigation of the current assembly process with the use of the LSS technique indicates that the process efficiency is low due to waste generation. The results obtained showed significant improvement in the process cycle efficiency (PCE) by 46.8%, via the implementation of the Kaizen continuous process improvement approach, 27.9% reduction in the lead-time, 59.3% increase in the value added time and 71.9% reduction in the non-value added time after the implementation of the LSS approach. The findings of this work demonstrated the feasibility of the LSS approach for waste minimisation and process performance improvement in a bid to achieve operational excellence in a manufacturing organisation.

Indexed as

Assembly processLSS approachOperational efficiencyPCEWaste reduction

Identifiers

PMID35287321
PMCPMC8917301

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.