TY  - CONF
TI  - A minimal-data toolkit for diagnosing loss of flow resilience in interior fit-out work
AU  - Asmone, Ashan Senel
AU  - Murguia, Danny
AU  - Ling, Zhengyang
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  - Assistant Research Professor, Department of Engineering, University of Cambridge, UK, dem52@cam.ac.uk,  orcid.org/0000-0003-1009-4058
AD  - Research Associate, Department of Engineering, University of Cambridge, UK, zl461@cam.ac.uk, orcid.org/0009-0005-6755-1741
AD  - Lecturer, Department of Civil Engineering, University of Moratuwa, Sri Lanka, asithar@uom.lk  orcid.org/0000-0002-1389-7801
AD  - Emeritus Professor, Department of Engineering, University of Cambridge, UK, orcid.org/0000-0002-9672-0680
ED  - Hamzeh, Farook
ED  - Poshdar, Mani
ED  - Garcia-Lopez,, Nelly P.
ED  - Gan, Vincent
PY  - 2026
DA  - 2026/06/22
T2  - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)
C3  - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)
CY  - Singapore, Singapore
SP  - 1499
EP  - 1510
DO  - 10.24928/2026/0125
SN  - 2789-0015 (ISSN)
AB  - 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.
KW  - Flow
KW  - waste
KW  - production variability
KW  - Location-Based Management (LBM)
KW  - Work in Progress/process (WIP).
L1  - https://www.iglc.net/papers/details/2453/pdf
UR  - https://www.iglc.net/papers/details/2453
DB  - IGLC.net
LA  - English
N1  - Export date: 04 October 2026
ER  -