30th Annual Conference of the International Group for Lean Construction (IGLC) - Edmonton, Canada - 2022

https://doi.org/10.24928/2022/0206

LPS Performance Diagnosis Model Using Fuzzy Inference System

Lynn Shehab1, Elyar Pourrahimian2, Diana Salhab3 & Farook Hamzeh4

1 Ph.D. Student, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada, [email protected], orcid.org/0000-0002-2708-3550
2Ph.D. Student, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada, [email protected], orcid.org/0000-0003-0035-2324
3Ph.D. Student, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada, [email protected], orcid.org/0000-0003-0307-6193
4Associate Professor, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada, [email protected], orcid.org/0000-0002-3986-9534

Abstract

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.

Keywords

Last Planner® System, fuzzy logic, implementation evaluation, diagnosis model, design science research.

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Reference

Shehab, L. , Pourrahimian, E. , Salhab, D. & Hamzeh, F. 2022. LPS Performance Diagnosis Model Using Fuzzy Inference System, Proc. 30th Annual Conference of the International Group for Lean Construction (IGLC) , 961-972. doi.org/10.24928/2022/0206

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