An assessment of the causes of schedule and cost overruns in megaprojects using Z-BWM-MARCOS integrated method

Document Type : Original Article

Authors

1 Ph.D. Candidate, Industrial Engineering Department, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

2 Assistant Professor, Industrial Engineering Department, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

3 Associate Professor, Industrial Engineering Department, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

10.48308/jimp.2025.236548.1569

Abstract

Schedule and cost overruns are the most common challenges in megaprojects around the world. Cost increase compared to the planned budget and megaproject time increase compared to the schedule are the reasons for the failure of many megaprojects. The aim of this research is to provide an integrated framework for evaluating causes of schedule and cost overrins in megaprojects. In this research, for the first time in order to analyze the causes, the integrated approach of the FMEA method based on the BWM and MARCOS methods developed based on Z-number theory in three phases has been used. In the first phase of this approach and based on the literature, the causes are identified using the FMEA method and the factors determining the RPN are quantified. In the second phase, using the Z-BWM method, the weights of the factors are calculated. Then in the third phase and according to the outputs of the previous phases, the causes are prioritized using the Z-MARCOS method. In addition to assigning different weights to factors and considering uncertainty, reliability is also considered in this approach through the theory of Z numbers. The proposed approach of this research was implemented in evaluating the causes of schedule and cost overruns. Based on the results, poor planning and scheduling, shortage of skilled labour and poor design of suppliers collaboration network have been identified as major causes. The results of the proposed approach have shown its capability and superiority compared to other methods such as FMEA and Fuzzy MARCOS.

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