Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study

Mahir Msawil, Faris Elghaish, Krisanthi Seneviratne and Stephen McIlwaine
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Mahir Msawil: School of Energy, Geoscience, Infrastructure and Society, Heriot Watt University Dubai Campus, Dubai Knowledge Park, Dubai P.O. Box 38103, United Arab Emirates
Faris Elghaish: School of Natural and Built Environment, Queen’s University Belfast, Belfast BT9 5AG, UK
Krisanthi Seneviratne: School of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW 2751, Australia
Stephen McIlwaine: School of Natural and Built Environment, Queen’s University Belfast, Belfast BT9 5AG, UK

Sustainability, 2021, vol. 13, issue 20, 1-26

Abstract: Forecasting the cash flow for infrastructure projects has not received much attention in the existing models. Moreover, disregarding the cost flow behaviour and proposing models that entail a relatively high dimensionality of inputs have been the main drawbacks of the existing models. This study proposes a heuristic cash flow forecasting (CFF) model for infrastructure projects, and it explores the underlying behaviour of the cost flow. The proposed model was validated by adopting a case study approach,the actual cost flow datasets were mined from a verified data system. The results invalidated the employment of a dominant heuristic rule with regard to a cost-flow-time relationship in infrastructure projects. On the other hand, a mathematical parameter-based comparison between the trends analysed from previous studies revealed that the cost flows of infrastructure projects procured through a design-bid-build (D-B-B) route behaved in a similar manner to building projects procured through a construction management route. This research contributes to the body of knowledge providing a method to enable infrastructure contractors to accurately forecast the required working capital through adding a new dimension for project classification by coining the term “ the quaternary flow percentage ”. In addition, this study indicates the importance of identifying the impact of root risks on the individual cost flow components rather than on the aggregated cost flow, which is a recommendation for future research.

Keywords: cash flow forecasting; cost flow components; risk; UAE infrastructure; heuristic modelling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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