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EXPORT DATE: 2 June 2025

@CONFERENCE{Madushanka2025,
author={Madushanka, Malik and Fiirgaard, Tobias and Pérez, Cristina T. },
editor={Seppänen, Olli  and Koskela, Lauri  and Murata , Koichi  },
title={Workflow Issue Identification by Applying Location-based Work Sampling}, 
journal={Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)}, 
booktitle={Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)}, 
year={2025},
pages={587-597},
url={http://www.iglc.net/papers/details/2322},
doi={10.24928/2025/0126},
affiliation={Master student, Department of Civil & Architectural Engineering (CAE), Aarhus University (AU), malikmadushanka52@gmail.com, https://orcid.org/0009-0006-2562-5121, ; Master student, CAE Department, AU, Denmark, tobias.fiirgaard@gmail.com, https://orcid.org/0009-0002-4078-3372 ; Tenure Track Assistant Professor, CAE Department, AU, Denmark, cristina.toca.perez@cae.au.dk, https://orcid.org/0000-0002-4182-1492 },
abstract={This study examines Location-Based Work Sampling (LBWS) to improve construction site management by identifying workflow issues. For that, a Case Study was conducted on a Danish new building project, and the workflows of three trades were observed. The LBWS application presented a structured four-step methodology: (1) activity and workspace identification; (2) data collection; (3) data visualization; and (4) data analysis. For the LBWS application, the “Ajour system” app was selected as the optimal software based on the technique “Choosing by Advantages”. At the data collection step, the authors registered 994 random observations of worker activities classified into six work activity categories (production, preparation, transportation, walking, talking, and waiting) and into four workspace categories (production, preparation, storage, and transportation workspace). Previous LBWS studies used a single workspace categorization. This study introduces a grid-based system to capture production dynamics. This study highlights LBWS as a powerful tool for optimizing workflows by identifying inefficiencies. It provides insights into (1) work activity analysis,  differentiating value-added vs. non-value-added tasks, reducing excessive walking and waiting times; (2) workspace and resource utilization, using heat maps to assess labor and material distribution; and (3) interferences and congestion, identifying workflow bottlenecks caused by overcrowding or mismanagement. },
author_keywords={Location-based Work Sampling, workflow, heat map, job site. },
address={Osaka and Kyoto, Japan },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}