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Article: Adopting lean thinking in virtual reality-based personalized operation training using value stream mapping

TitleAdopting lean thinking in virtual reality-based personalized operation training using value stream mapping
Authors
KeywordsLean
Personalized training
Productivity
Value stream mapping
Virtual reality
Issue Date2020
Citation
Automation in Construction, 2020, v. 119, article no. 103355 How to Cite?
AbstractLean thinking has been proven effective in helping practitioners identify and eliminate wastes during engineering operations. However, systematic instructional mechanisms and training protocols based on individual trainee's performance are insufficient in existing training to define value-added activities for further productivity improvement in a training environment. This study aims to investigate how value stream mapping (VSM), as a lean tool, can be applied to help improve operation training performances through an immersive virtual reality (VR)-based personalized training program. A before–after experiment based on a virtual scaffolding erection scenario is established to simulate the training process. The training performance resulting from the VSM-based VR approach is compared with conventional VR training. Comparative results indicate that the waste time and errors reduce significantly. Compared with the conventional method, the overall productivity improvement of the erection process using VSM-based VR training is 12%. This demonstrates that integrating lean thinking into the operation training process can be a more effective approach for VR-based personalized operation training, provided that appropriate instructions are implemented.
Persistent Identifierhttp://hdl.handle.net/10722/326230
ISSN
2023 Impact Factor: 9.6
2023 SCImago Journal Rankings: 2.626
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Peng-
dc.contributor.authorWu, Peng-
dc.contributor.authorChi, Hung Lin-
dc.contributor.authorLi, Xiao-
dc.date.accessioned2023-03-09T09:59:04Z-
dc.date.available2023-03-09T09:59:04Z-
dc.date.issued2020-
dc.identifier.citationAutomation in Construction, 2020, v. 119, article no. 103355-
dc.identifier.issn0926-5805-
dc.identifier.urihttp://hdl.handle.net/10722/326230-
dc.description.abstractLean thinking has been proven effective in helping practitioners identify and eliminate wastes during engineering operations. However, systematic instructional mechanisms and training protocols based on individual trainee's performance are insufficient in existing training to define value-added activities for further productivity improvement in a training environment. This study aims to investigate how value stream mapping (VSM), as a lean tool, can be applied to help improve operation training performances through an immersive virtual reality (VR)-based personalized training program. A before–after experiment based on a virtual scaffolding erection scenario is established to simulate the training process. The training performance resulting from the VSM-based VR approach is compared with conventional VR training. Comparative results indicate that the waste time and errors reduce significantly. Compared with the conventional method, the overall productivity improvement of the erection process using VSM-based VR training is 12%. This demonstrates that integrating lean thinking into the operation training process can be a more effective approach for VR-based personalized operation training, provided that appropriate instructions are implemented.-
dc.languageeng-
dc.relation.ispartofAutomation in Construction-
dc.subjectLean-
dc.subjectPersonalized training-
dc.subjectProductivity-
dc.subjectValue stream mapping-
dc.subjectVirtual reality-
dc.titleAdopting lean thinking in virtual reality-based personalized operation training using value stream mapping-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.autcon.2020.103355-
dc.identifier.scopuseid_2-s2.0-85088397028-
dc.identifier.volume119-
dc.identifier.spagearticle no. 103355-
dc.identifier.epagearticle no. 103355-
dc.identifier.isiWOS:000579044100011-

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