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EXPORT DATE: 19 April 2025

@CONFERENCE{Saini2023,
author={Saini, Abhay and Thomas, Albert },
editor={ },
title={Development of a Machine Learning-Based Labour Productivity Prediction Tool to Practice Lean Construction}, 
journal={Proceedings of the 31st Annual Conference of the International Group for Lean Construction (IGLC31)}, 
booktitle={Proceedings of the 31st Annual Conference of the International Group for Lean Construction (IGLC31)}, 
year={2023},
pages={1326-1336},
url={http://www.iglc.net/papers/details/2142},
doi={10.24928/2023/0209},
affiliation={Master’s Student, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai,  Maharashtra 400076, India, 213040078@iitb.ac.in, http://orcid.org/0000-0001-5702-5880  ; Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai,  Maharashtra 400076, India, albert@iitb.ac.in, http://orcid.org/0000-0002-4924-6592 },
abstract={The construction industry is a labour-intensive industry. This is one of the reasons why the industry has significant room to incorporate lean principles and reduce waste. Various lean tools can be implemented in construction projects, such as Kanban, JIT and 5S. However, these tools majorly focus on activities at an aggregate level and do not always incorporate sub-activities carried out within a small activity. The productivity of smaller activities (activities that typically span from minutes to hours) is essential to be assessed and controlled to increase the efficiency of overall activity. This paper aims to develop a labour productivity prediction tool based on machine learning principles and lean ideologies to improve the overall productivity of construction activities, considering the productivity of sub-activities. The developed framework is demonstrated by analyzing the productivity of reinforcement activity in a construction project. In the study, inventory wastes are minimized using the prediction from the developed quantitative labour productivity prediction model. An increase of 13.7% in overall productivity is achieved through the implementation of the developed framework },
author_keywords={Lean construction process, value stream mapping, machine learning, lean theory },
address={Lille, France },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}