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

Peng Wang, Peng Wu, Hung Lin Chi, Xiao Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


Lean 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.

Original languageEnglish
Article number103355
JournalAutomation in Construction
Publication statusPublished - Nov 2020


  • Lean
  • Personalized training
  • Productivity
  • Value stream mapping
  • Virtual reality

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


Dive into the research topics of 'Adopting lean thinking in virtual reality-based personalized operation training using value stream mapping'. Together they form a unique fingerprint.

Cite this