TY  - CONF
TI  - Automated Data Capture and Analysis to Detect Process Waste in Interior Finishing Work
AU  - Asmone, Ashan Senel
AU  - Murguia, Danny
AU  - Rathnayake, Asitha
AU  - Middleton, Campbell
AD  - Research Associate, Department of Engineering, University of Cambridge, UK, asa79@cam.ac.uk, orcid.org/0000-0002-2173-3890
AD  - Senior Research Associate, Department of Engineering, University of Cambridge, UK, dem52@cam.ac.uk, orcid.org/0000-0003-1009-4058
AD  - PhD Candidate, Department of Engineering, University of Cambridge, UK, agbrr2@cam.ac.uk, orcid.org/0000-0002-1389-7801
AD  - Professor, Department of Engineering, University of Cambridge, UK, prof@construction.cam.ac.uk, orcid.org/0000-0002-9672-0680
ED  - Costa, Dayana Bastos
ED  - Drevland, Frode
ED  - Florez-Perez, Laura
PY  - 2024
DA  - 2024/07/01
T2  - Proceedings of the 32nd Annual Conference of the International Group for Lean Construction (IGLC 32)
C3  - Proceedings of the 32nd Annual Conference of the International Group for Lean Construction (IGLC 32)
CY  - Auckland, New Zealand
SP  - 660
EP  - 671
DO  - 10.24928/2024/0135
SN  - 2789-0015 (ISSN)
AB  - 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.
KW  - Flow
KW  - process
KW  - waste
KW  - location-based management (LBM)
KW  - work in progress/process (WIP).
L1  - https://www.iglc.net/papers/details/2221/pdf
UR  - https://www.iglc.net/papers/details/2221
DB  - IGLC.net
LA  - English
N1  - Export date: 04 October 2026
ER  -