TY  - CONF
TI  - Development of a Machine Learning-Based Labour Productivity Prediction Tool to Practice Lean Construction
C1  - Lille, France
C3  - Proceedings of the 31st Annual Conference of the International Group for Lean Construction (IGLC31)
SP  - 1326
EP  - 1336
PY  - 2023
DO  - 10.24928/2023/0209
AU  - Saini, Abhay
AU  - Thomas, Albert
AD  - 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 
AD  - 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
AB  - 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
KW  - Lean construction process
KW  - value stream mapping
KW  - machine learning
KW  - lean theory
PB  - 
T2  - Proceedings of the 31st Annual Conference of the International Group for Lean Construction (IGLC31)
DA  - 2023/06/26
CY  - Lille, France
L1  - http://iglc.net/Papers/Details/2142/pdf
L2  - http://iglc.net/Papers/Details/2142
N1  - Export Date: 24 April 2025
DB  - IGLC.net
DP  - IGLC
LA  - English
ER  -