The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma

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dc.citation.volume9
dc.contributor.authorPerera AD
dc.contributor.authorJayamaha NP
dc.contributor.authorGrigg NP
dc.contributor.authorTunnicliffe M
dc.contributor.authorSingh A
dc.date.available2021
dc.date.issued17/08/2021
dc.description.abstractLean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.
dc.description.publication-statusPublished
dc.format.extent112411 - 112424
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000686455700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationIEEE ACCESS, 2021, 9 pp. 112411 - 112424
dc.identifier.doi10.1109/ACCESS.2021.3103931
dc.identifier.elements-id448071
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10179/16573
dc.publisherIEEE
dc.relation.isPartOfIEEE ACCESS
dc.subjectSix sigma
dc.subjectOrganizations
dc.subjectProject management
dc.subjectNatural language processing
dc.subjectDeep learning
dc.subjectCustomer satisfaction
dc.subjectManufacturing
dc.subjectArtificial intelligence
dc.subjectcritical success factors (CSFs) of LSS
dc.subjectlean
dc.subjectlean six sigma (LSS)
dc.subjectsix sigma (SS)
dc.subjectdeep neural network
dc.subjectword embedding
dc.subjectclassification model
dc.subject.anzsrc08 Information and Computing Sciences
dc.subject.anzsrc09 Engineering
dc.subject.anzsrc10 Technology
dc.titleThe Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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