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EXPORT DATE: 28 April 2024

@CONFERENCE{Wandahl2022,
author={Wandahl, Søren  and Pérez, Cristina Toca  and Salling, Stephanie  and Lerche, Jon  },
editor={ },
title={Robustness of Work Sampling for Measuring Time Waste}, 
journal={Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC)}, 
booktitle={Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC)}, 
year={2022},
pages={247-258},
url={http://www.iglc.net/papers/details/1961},
doi={10.24928/2022/0127},
affiliation={Professor, Department of Civil & Architectural Engineering, Aarhus University, Denmark,  swa@cae.au.dk, https://orcid.org/0000-0001-8708-6035 ; Postdoc, Department of Civil & Architectural Engineering, Aarhus University, Denmark,  cristina.toca.perez@cae.au.dk, https://orcid.org/0000-0002-4182-1492 ; Research Assistant, Department of Civil & Architectural Engineering, Aarhus University, Denmark,  stsa@cae.au.dk, https://orcid.org/0000-0001-7088-6458  ; Postdoc, Department of Business Development and Technology, Aarhus University, Denmark,  jon.lerche@btech.au.dk, https://orcid.org/0000-0001-7076-9630 },
abstract={Construction can be considered a socio-technical system, which is challenging to model due to the many agents interacting either in a managed way or autonomously. Therefore, cause and effect models are hard to validate, and a traditional correlation approach is insufficient. In this study, the method of robustness testing was applied to test the effect stability when assumptions of a model are changed. The research objective is to apply robustness testing on WS data to assess the robustness and validity of the WS method. An actual refurbishment project was the case for this study, where data was acquired through nine days of continuous WS application. Time-series data were grouped into Direct Work (DW), Indirect Work, and Waste Work. Several different robustness tests were applied. It can be concluded that the WS method is robust, i.e., the effect (DW) is stable even if the assumptions are changed severely. Deleting 90% of the sample does, for instance, almost not change the effect. Likewise, if errors are infused into the sample, the effect is stable. Also, if certain structural parts are excluded from the sample, e.g., observations during morning startup, etc., the effect is still stable.  },
author_keywords={Value stream, Waste, Trust, Robustness, Work Sampling },
address={Edmonton, Canada },
issn={2309-0979 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}