Location-based Work Sampling

Location-based Work Sampling

2023

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DOI: https://doi.org/10.60164/6ysp7wio4

Authors: Cristina T. Pérez, Stephanie T. Salling, Søren Wandahl

Citation:

Pérez, C. T., Salling, S. T., Wandahl, S. (2023) Location-based Work Sampling. Lean Construction Journal 2023. pp 69-81

Abstract:

Question: Which opportunities can merging geographic location data with Work Sampling (WS) data bring for construction management?

Purpose: To identify which opportunities adding geographic information to the random observations made (named in this study as geo-located observations) can bring.

Research Method: The authors presented the implementation of a novel adaptation of the WS technique, named Location-Based Work Sampling (LBWS), 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.

Findings: LBWS consists of a visual graphical approach that facilitates sharing information obtained during the WS application, based on adding geo-location information to the random observations. The technique shows the observations made on construction trades and work categories in the foreground and job site spaces in the background.

Limitations: The use of geographic coordinates provided by the photos limits the application of this technique to outdoor activities.

Implications: Creating a relatively simple visualization of the observations on the job site layout showed that a more comprehensive analysis of the job site activities could be conducted.

Value for practitioners: The LBWS technique can provide a better understanding of the ongoing activities’ behavior and contribute to the existing Location-Based Management Systems.

Keywords: Location-based Management, Visual Management, Work Sampling.

Paper type: Case Study