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EXPORT DATE: 3 April 2025

@CONFERENCE{Asmone2024,
author={Asmone, Ashan Senel and Murguia, Danny and Rathnayake, Asitha and Middleton, Campbell },
editor={ },
title={Automated Data Capture and Analysis to Detect Process Waste in Interior Finishing Work}, 
journal={Proceedings of the 32nd Annual Conference of the International Group for Lean Construction (IGLC 32)}, 
booktitle={Proceedings of the 32nd Annual Conference of the International Group for Lean Construction (IGLC 32)}, 
year={2024},
pages={660-671},
url={http://www.iglc.net/papers/details/2221},
doi={10.24928/2024/0135},
affiliation={Research Associate, Department of Engineering, University of Cambridge, UK, asa79@cam.ac.uk, orcid.org/0000-0002-2173-3890 ; Senior Research Associate, Department of Engineering, University of Cambridge, UK, dem52@cam.ac.uk, orcid.org/0000-0003-1009-4058 ; PhD Candidate, Department of Engineering, University of Cambridge, UK, agbrr2@cam.ac.uk, orcid.org/0000-0002-1389-7801 ; Professor, Department of Engineering, University of Cambridge, UK, prof@construction.cam.ac.uk, orcid.org/0000-0002-9672-0680 },
abstract={Detecting process waste in complex production systems is still a challenge in construction projects. The integration of lean construction with automated data capturing technologies presents an opportunity to timely detect process waste and steer projects towards targets. By using vision-based technology for automated progress monitoring in a residential building, this study examines interior finishing work activities through the lenses of process/location flow and operations/trade flow. Location-based management tools (flowlines and line-of-balance) were used to visualise the data. Results showed that planned production deviated from actuals in all activities. Significant variability was observed within the completion of each activity at each location. The ratio between average production rate and exemplar performance indicated a missed opportunity to improve project performance. Resultantly, several waste types were identified including inefficient work, space not being worked in, unnecessary movement of people and unnecessary transportation of materials. The ability to actively pinpoint process waste provides managers with a granular understanding of inefficiencies, enabling targeted interventions to enhance productivity and reduce waste. The findings support that automated data capturing and analytics through the lenses of lean construction is a useful strategy to inform construction programmes to be more realistic, improving upon efficiency and waste reduction. },
author_keywords={Flow, process, waste, location-based management (LBM), work in progress/process (WIP). },
address={Auckland, New Zealand },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}