IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v17y1999i6p745-755.html
   My bibliography  Save this article

Applying fuzzy techniques to cash flow analysis

Author

Listed:
  • A. H. Boussabaine
  • Taha Elhag

Abstract

Construction managers are interested in the direction of movement of cash flow at valuation periods rather than its forecast value, and fuzzy set theory applied to decision making might help in this process. Fuzzy models are particularly suited to making decisions involving new technologies where uncertainties inherent in the situation are complex. The problem of healthy cash flow at valuation periods relates to the proper estimation of cash in and out flows and project progress. The paper presents an alternative approach to cash flow analysis for construction projects. This project is based on the assumption that cash flow at particular valuation stages of a project is ambiguous. The paper discusses the weaknesses of existing methods for cash flow and establishes the need for an alternative approach. Using an example of 30 cash flow curves, the advantage of fuzzy cash flow analysis is demonstrated. Results of the analysis are presented and discussed. The model can be used to analyse the cash flow curve of projects at any progress period to make sure it is reasonable.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:conmgt:v:17:y:1999:i:6:p:745-755
    DOI: 10.1080/014461999371088
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/014461999371088
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/014461999371088?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. Piasecki Krzysztof & Siwek Joanna, 2015. "Behavioural Present Value Defined as Fuzzy Number – a New Approach," Folia Oeconomica Stetinensia, Sciendo, vol. 15(2), pages 27-41, December.
    4. 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.
    5. 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).
    6. Tolga, Ethem & Demircan, Murat Levent & Kahraman, Cengiz, 2005. "Operating system selection using fuzzy replacement analysis and analytic hierarchy process," International Journal of Production Economics, Elsevier, vol. 97(1), pages 89-117, July.
    7. Beskese, Ahmet & Kahraman, Cengiz & Irani, Zahir, 2004. "Quantification of flexibility in advanced manufacturing systems using fuzzy concept," International Journal of Production Economics, Elsevier, vol. 89(1), pages 45-56, May.
    8. Wright, Daniel G. & Dey, Prasanta K. & Brammer, John G., 2013. "A fuzzy levelised energy cost method for renewable energy technology assessment," Energy Policy, Elsevier, vol. 62(C), pages 315-323.

    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. 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.
    2. 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.
    3. 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).
    4. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    5. 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.

    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:taf:conmgt:v:17:y:1999:i:6:p:745-755. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RCME20 .

    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.