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A Data Analytics Approach for Forecasting Cash Flow in Construction Projects
Mahmoud, Hasan S.
Mahmoud, Hasan S.
Description
A Doctor of Philosophy Dissertation in Engineering Systems Management by Hasan S. Mahmoud entitled, “A Data Analytics Approach for Forecasting Cash Flow in Construction Projects”, submitted in March 2021. Dissertation advisor is Dr. Vian Ahmed and dissertation co-advisor is Dr. Salwa M. Beheiry. Soft copy is available (Thesis, Completion Certificate, and AUS Archives Consent Form).
Abstract
The construction industry is one of the most crucial sectors in any economy. The construction industry leads the development of the underlying infrastructure in all regions, including the industrial, transportation, environmental, and commercial elements. The construction industry also fosters and executes capital-intensive projects that strengthen governmental and multinational corporate performance. Furthermore, the construction industry is considered the largest employer worldwide, engaging and training various technical and vocational expertise. Hence, it is vital that the industry maintains its financial, competitive advantage and sustains its operations. This competitive advantage is the aggregate performance of individual projects and collective programs. As such, there is a continuous and robust need for owners/developers to more accurately forecast, monitor, and control project performance. Many tools have been developed to monitor both the cost and the schedule performance of construction projects. Nevertheless, most lacked a comprehensive integration of risk factor quantification tools and the inclusion of owner-tailored input, particularly in cash flow monitoring and prediction. Therefore, this research aims to develop an "owner perspective" cash flow prediction framework that uses project performance data, and employs iterative fuzzy stochastic techniques to construct and model cost data to make more accurate cash flow estimates. To that aim, the study created, validated and tested a Cash Flow Risk Index (CFRI) that integrated relevant risk factors and collected and modeled project data to analyze contractors' default probabilities and predict time and cost overruns. Moreover, the study also created a metric to measure the response of project owners towards the identified risk and their response strategy. The research also designed an interactive tool for owner/developers to use on single/multiple projects or comprehensive programs. The developed model also allows the monitoring and controlling of the financial performance of the project during the time of operations. The results of the case study investigated here showed that the better quantification of risk factors leads to more accurate cash flow estimates by an increase in cash flow estimation accuracy of more than 30%.