TY  - CONF
TI  - LPS Performance Diagnosis Model Using Fuzzy Inference System
C1  - Edmonton, Canada
C3  - Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC)
SP  - 961
EP  - 972
PY  - 2022
DO  - 10.24928/2022/0206
AU  - Shehab, Lynn 
AU  - Pourrahimian, Elyar 
AU  - Salhab, Diana 
AU  - Hamzeh, Farook 
AD  -  Ph.D. Student, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada,  lshehab@ualberta.ca, orcid.org/0000-0002-2708-3550 
AD  - Ph.D. Student, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada,  elyar@ualberta.ca, orcid.org/0000-0003-0035-2324
AD  - Ph.D. Student, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada,  salhab@ualberta.ca, orcid.org/0000-0003-0307-6193
AD  - Associate Professor, Hole School of Construction Engineering, University of Alberta, Edmonton,  Canada, hamzeh@ualberta.ca, orcid.org/0000-0002-3986-9534 
AB  - The Last Planner System (LPS) has long been used in construction projects to promote reliable planning and enhance productivity. However, despite various attempts to evaluate LPS implementation efforts, the human aspect of the evaluation attempts has not been given enough attention. This issue may be tackled through Fuzzy Inference Systems (FIS) to capture more information regarding the gradual and intricate changes in scoring systems. Therefore, this paper aims to offer a standardized diagnosis model for LPS performance in construction projects. This model employs an FIS that analyzes the results of an LPS implementation for a more accurate investigation of the implementation. First, a thorough literature review is conducted to select the most prominent factors influencing the LPS implementation process, followed by expert panel questionnaire development and distribution among LPS experts to rank the selected factors. The obtained questionnaire results are then used to develop the FIS. The objective of this paper is hereby twofold: (1) to allow assessing expected LPS benefits through the qualitative assessment of the performance in the four LPS phases, and (2) to facilitate comparing past, current, and future performances throughout the organization's LPS implementation process to ensure continuous improvement. 
KW  - Last Planner® System
KW  - fuzzy logic
KW  - implementation evaluation
KW  - diagnosis model
KW  - design  science research.
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/2022/pdf
L2  - http://iglc.net/Papers/Details/2022
N1  - Export Date: 28 April 2024
DB  - IGLC.net
DP  - IGLC
LA  - English
ER  -