IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i20p11305-d655258.html

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mahir Msawil & Faris Elghaish & Krisanthi Seneviratne & Stephen McIlwaine, 2021. "Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study," Sustainability, MDPI, vol. 13(20), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11305-:d:655258
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/20/11305/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/20/11305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuvraj Gajpal & Ashraf Elazouni, 2015. "Enhanced heuristic for finance-based scheduling of construction projects," Construction Management and Economics, Taylor & Francis Journals, vol. 33(7), pages 531-553, July.
    2. Farzad Khosrowshahi & Amir Alani, 2003. "A model for smoothing time-series data in construction," Construction Management and Economics, Taylor & Francis Journals, vol. 21(5), pages 483-494.
    3. Bent Flyvbjerg, 2009. "Survival of the unfittest: why the worst infrastructure gets built--and what we can do about it," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 25(3), pages 344-367, Autumn.
    4. Taleb, Nassim Nicholas, 2019. "How much data do you need? An operational, pre-asymptotic metric for fat-tailedness," International Journal of Forecasting, Elsevier, vol. 35(2), pages 677-686.
    5. Lauri Koskela, 2017. "Why is management research irrelevant?," Construction Management and Economics, Taylor & Francis Journals, vol. 35(1-2), pages 4-23, February.
    6. Ashraf Elazouni, 2009. "Heuristic method for multi-project finance-based scheduling," Construction Management and Economics, Taylor & Francis Journals, vol. 27(2), pages 199-211.
    7. A. H. Boussabaine & Taha Elhag, 1999. "Applying fuzzy techniques to cash flow analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 17(6), pages 745-755.
    8. Dominic D. Ahiaga-Dagbui & Simon D. Smith, 2014. "Dealing with construction cost overruns using data mining," Construction Management and Economics, Taylor & Francis Journals, vol. 32(7-8), pages 682-694, August.
    9. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    10. Yi Su & Gunnar Lucko, 2015. "Synthetic cash flow model with singularity functions for unbalanced bidding scenarios," Construction Management and Economics, Taylor & Francis Journals, vol. 33(1), pages 35-54, January.
    11. Huan Neng Chiu & Deng Maw Tsai, 2002. "An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows," Construction Management and Economics, Taylor & Francis Journals, vol. 20(1), pages 55-66.
    12. A. H. Boussabaine & A. P. Kaka, 1998. "A neural networks approach for cost flow forecasting," Construction Management and Economics, Taylor & Francis Journals, vol. 16(4), pages 471-479.
    13. Henry A. Odeyinka & John Lowe & Ammar P. Kaka, 2013. "Artificial neural network cost flow risk assessment model," Construction Management and Economics, Taylor & Francis Journals, vol. 31(5), pages 423-439, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Qingbin Cui & Makarand Hastak & Daniel Halpin, 2010. "Systems analysis of project cash flow management strategies," Construction Management and Economics, Taylor & Francis Journals, vol. 28(4), pages 361-376.
    3. Shu-Shun Liu & Chang-Jung Wang, 2009. "Two-stage profit optimization model for linear scheduling problems considering cash flow," Construction Management and Economics, Taylor & Francis Journals, vol. 27(11), pages 1023-1037.
    4. Yukang He & Tao Jia & Weibo Zheng, 2024. "Simulated annealing for centralised resource-constrained multiproject scheduling to minimise the maximal cash flow gap under different payment patterns," Annals of Operations Research, Springer, vol. 338(1), pages 115-149, July.
    5. Yuvraj Gajpal & Ashraf Elazouni, 2015. "Enhanced heuristic for finance-based scheduling of construction projects," Construction Management and Economics, Taylor & Francis Journals, vol. 33(7), pages 531-553, July.
    6. Love, Peter E.D. & Ahiaga-Dagbui, Dominic D., 2018. "Debunking fake news in a post-truth era: The plausible untruths of cost underestimation in transport infrastructure projects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 357-368.
    7. Love, Peter E.D. & Ika, Lavagnon A. & Ahiaga-Dagbui, Dominic D., 2019. "On de-bunking ‘fake news’ in a post truth era: Why does the Planning Fallacy explanation for cost overruns fall short?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 397-408.
    8. Winkler, Lorenz & Kilic, Onur A. & Veldman, Jasper, 2022. "Collaboration in the offshore wind farm decommissioning supply chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Andrew Feltenstein & Nour Abdul-Razzak & Jeffrey Condon & Biplab Kumar Datta, 2015. "Tax Evasion, the Provision of Public Infrastructure and Growth: A General Equilibrium Approach to Two Very Different Countries, Egypt and Mauritius," Journal of African Economies, Centre for the Study of African Economies, vol. 24(suppl_2), pages 43-72.
    10. Cheng, Min-Yuan & Vu, Quoc-Tuan, 2024. "Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management," Energy, Elsevier, vol. 302(C).
    11. Tsekeris, Theodore, 2014. "Multi-sectoral interdependencies of regional public infrastructure investments," Socio-Economic Planning Sciences, Elsevier, vol. 48(4), pages 263-272.
    12. Martijn Leijten, 2013. "Real-world decision-making on mega-projects: politics, bias and strategic behaviour," Chapters, in: Hugo Priemus & Bert van Wee (ed.), International Handbook on Mega-Projects, chapter 4, pages 57-82, Edward Elgar Publishing.
    13. Rodríguez-Pose, Andrés & Bartalucci, Federico & Lozano-Gracia, Nancy & Dávalos, María, 2024. "Overcoming left-behindedness. Moving beyond the efficiency versus equity debate in territorial development," LSE Research Online Documents on Economics 125629, London School of Economics and Political Science, LSE Library.
    14. Xin J. Ge & G. Runeson, 2004. "Modeling Property Prices Using Neural Network Model for Hong Kong," International Real Estate Review, Global Social Science Institute, vol. 7(1), pages 121-138.
    15. Arias, Juan F. & Bachmann, Chris, 2022. "Quantifying the relative importance of rapid transit implementation barriers: Evidence from ecuador," Journal of Transport Geography, Elsevier, vol. 100(C).
    16. Puisa, Romanas & Montewka, Jakub & Krata, Przemyslaw, 2023. "A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    17. Rebecca Vine, 2020. "Riskwork in the construction of Heathrow Terminal 2," SPRU Working Paper Series 2020-20, SPRU - Science Policy Research Unit, University of Sussex Business School.
    18. Eliasson, Jonas & Fosgerau, Mogens, 2013. "Optimism bias in project appraisal: deception or selection?," Working papers in Transport Economics 2013:6, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    19. Bent Flyvbjerg, 2014. "What You Should Know About Megaprojects, and Why: An Overview," Papers 1409.0003, arXiv.org.
    20. Mills Russell W., 2013. "Congressional modification of benefit-cost analysis as a vehicle for particularized benefits and a limitation on agency discretion: the case of the federal contract tower program," Journal of Benefit-Cost Analysis, De Gruyter, vol. 4(3), pages 301-333, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11305-:d:655258. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.