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EXPORT DATE: 19 June 2026

@CONFERENCE{Asmone2026,
author={Asmone, Ashan Senel and Murguia, Danny and Ling, Zhengyang and Rathnayake, Asitha and Middleton, Campbell },
editor={Hamzeh, Farook and Poshdar, Mani and Garcia-Lopez,, Nelly P. },
title={A minimal-data toolkit for diagnosing loss of flow resilience in interior fit-out work}, 
journal={Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)}, 
booktitle={Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)}, 
year={2026},
pages={1499-1510},
url={http://www.iglc.net/papers/details/2453},
doi={10.24928/2026/0125},
affiliation={Research Associate, Department of Engineering, University of Cambridge, UK, asa79@cam.ac.uk, orcid.org/0000-0002-2173-3890 ; Assistant Research Professor, Department of Engineering, University of Cambridge, UK, dem52@cam.ac.uk,  orcid.org/0000-0003-1009-4058 ; Research Associate, Department of Engineering, University of Cambridge, UK, zl461@cam.ac.uk, orcid.org/0009-0005-6755-1741 ; Lecturer, Department of Civil Engineering, University of Moratuwa, Sri Lanka, asithar@uom.lk  orcid.org/0000-0002-1389-7801 ; Emeritus Professor, Department of Engineering, University of Cambridge, UK, orcid.org/0000-0002-9672-0680 },
abstract={This paper proposes and empirically tests a minimal flow performance measurement toolkit for construction fit-out works, designed to characterise flow stability, continuity, and vulnerability to schedule disruption using only routinely collected progress observations. The toolkit operates across activity, trade, and location-levels, requiring no detailed plans or bespoke data capture infrastructure. Drawing on lean construction flow theory and empirical evidence linking variability, discontinuity, and performance loss, the framework generates interpretable diagnostics available during project execution. A case study of interior fit-out works across 11 floors, 111 apartments, 21 activities, and 4 trades were selected. Weekly activity progress was captured using visual data analysed with computer vision. Results demonstrate that similar mean outputs can mask radically different flow patterns. Large schedule slippage is associated with extreme flow instability signatures rather than any single metric. The contribution is a standardisable, empirically grounded diagnostic toolkit that lowers the data and tooling barrier for flow-based performance assessment, supporting managerial sensemaking and early intervention rather than prediction. },
author_keywords={Flow, waste, production variability, Location-Based Management (LBM), Work in Progress/process (WIP). },
address={Singapore, Singapore },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}