WORKFLOW ISSUE IDENTIFICATION BY APPLYING LOCATION-BASED WORK SAMPLING

TY - GEN

T1 - WORKFLOW ISSUE IDENTIFICATION BY APPLYING LOCATION-BASED WORK SAMPLING

AU - Madushanka, Malik

AU - Fiirgaard, Tobias

AU - Pérez, Cristina T.

N1 - Publisher Copyright: © 2025, International Group for Lean Construction. All rights reserved.

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - heat map

KW - job site

KW - Location-based Work Sampling

KW - workflow

UR - https://www.scopus.com/pages/publications/105007084728

U2 - 10.24928/2025/0126

DO - 10.24928/2025/0126

M3 - Conference article

AN - SCOPUS:105007084728

SN - 2309-0979

VL - 33

SP - 587

EP - 597

JO - Annual Conference of the International Group for Lean Construction, IGLC

JF - Annual Conference of the International Group for Lean Construction, IGLC

T2 - 33rd Annual Conference of the International Group for Lean Construction, IGLC 2025

Y2 - 2 June 2025 through 8 June 2025

ER -