TY  - CONF
TI  - Location-Based Work Sampling
C1  - Edmonton, Canada
C3  - Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC)
SP  - 187
EP  - 198
PY  - 2022
DO  - 10.24928/2022/0121
AU  - Pérez, Cristina T. 
AU  - Salling, Stephanie 
AU  - Wandahl, Søren 
AD  - Postdoc, Department of Civil & Architectural Engineering, Aarhus University, Denmark,  cristina.toca.perez@cae.au.dk, https://orcid.org/0000-0002-4182-1492
AD  - Research Assistant, Department of Civil & Architectural Engineering, Aarhus University, Denmark,  stsa@cae.au.dk, https://orcid.org/0000-0001-7088-6458
AD  - Professor, Department of Civil & Architectural Engineering, Aarhus University, Denmark,  swa@cae.au.dk, https://orcid.org/0000-0001-8708-6035 
AB  - Previous studies have applied the Work Sampling (WS) technique in different job sites to determine how workers employ their time in relation to a taxonomy of various work activities. However, no other significant contribution has been discussed for including location information of the work activities. This study added a geographic location to each random WS observation for a more comprehensive work efficiency analysis. In this paper, an implementation analysis was presented based on the findings from a case study. The research process followed four steps: (1) clarifying the categories of the activities; (2) deciding the confidence interval; (3) collecting and extracting data; and (4) analyzing the data. For adding location data to the technique, the authors used the geographic coordinates provided by smartwatches used by the research team connected to two Global Navigations Satellite Systems (GNSS), and the coordinates obtained from photos taken for each observation. Each observation made contained the following information: (1) photo; (2) timestamp; (3) trade observed; (4) work category; and (5) geographic coordinates, consequently, workspace category. This paper presents as the main contribution an adaption of the WS technique, named Location-based Work Sampling (LBWS), which can provide a better understanding of the ongoing activities’ behavior.
KW  - Location-based Management (LBM)
KW  - Visual Management
KW  - Waste
KW  - Work Sampling
KW  - Geographic location observations.
PB  - 
T2  - Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC)
DA  - 2022/07/27
CY  - Edmonton, Canada
L1  - http://iglc.net/Papers/Details/1956/pdf
L2  - http://iglc.net/Papers/Details/1956
N1  - Export Date: 28 April 2024
DB  - IGLC.net
DP  - IGLC
LA  - English
ER  -