Conference Paper

Combining Qualitative and Quantitative Factors in Risk Analysis of Cash Flow Dependent Infrastructure Projects

To read the full-text of this research, you can request a copy directly from the authors.


The evaluation of cash flow dependent infrastructure projects require the involvement of a large set of internal and external factors. Generally, the nature of these factors is both qualitative and quantitative. Usually, the risk analysis process is done with a characterizing methodology and/or mathematical model that requires only numerical variables. This forces the sponsor to separate the analysis of the project in two parts: an analysis of the qualitative information, which often results in a subjective and biased decision, and afterwards, an analysis of the quantitative information. Due to the separation of both analyses, it is possible that the subjective decision performed at the beginning is inaccurate, reducing the importance or eliminating the project as an investment opportunity. This paper examines the risks involved in these types of infrastructure projects and describes the application of a neurofuzzy system as a potential tool that could reduce the separation between decisions. An analysis of both qualitative and quantitative factors in a single tool will contribute to reduce the subjectivity and arbitrariness involved in risk analysis of cash flow dependent projects and provides a better support or framework to the decision-making process.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

Construction is a risky business with only 47% of startup businesses in construction operating after four years. The indirect costs of failed companies far exceed the direct costs of their failure. Cash is often seen as the most important element of construction companies and their operation. Adequate sources of capital, and a reasonable liabilities-to-assets ratio, are critical for business continuity and success. A lack of cash can mean no payments to subcontractors, laborers, and crews, and no purchases of needed materials. It can lead to limited ability to complete tasks on site, cutting corners in work, or slower pace to match the amount of cash available. Negative outcomes can include delayed or incomplete work or increased financing costs and project risks. Ultimately, construction companies risk failure if they sustain cash flow limitations for some time despite the fact they could be profitable. In this research, we developed a cash flow model for the assessment of construction companies??? operations and their potential for failure. The cash flow model describes a company???s operational strength using a cash flow cycle with three measures: 1) cash flow cycle profitability, 2) cash flow cycle duration, and 3) access to additional access. We theoretically establish the importance and justification for each measure. Using a dataset comprised of full quarterly financial records for construction companies tracked over 20 years, we validate the suitability of the cash flow model in predicting construction company failure 6 months, 1 year, and 2 years in advance of failure event at a statistically significant level.
Recent trends in the construction industry indicate continued use of alternative procurement methods such as design-build, construction management, build-operate-transfer, and privatization. Increased use of these evolving methods produces higher levels of uncertainty with respect to long term performance and profitability. The uncertainties inherent in implementing new procurement methods necessitate investigation of enhanced methods of pre-project planning and analysis. This is particularly true for revenue dependent privatization projects such as toll roads. Poor initial performance of toll road projects suggests traditional methods of project analysis are inadequate. Sustaining investor and stakeholder support of privatized revenue dependent projects is dependent upon successful financial performance. Enhanced risk analysis tools provide improved information for pre-project decision making and performance outcome. One such risk analysis method is the Monte Carlo. Monte Carlo methods are especially useful in evaluating which of several uncertain quantities most significantly contributes to the overall risk of the project. This paper demonstrates a Monte Carlo risk assessment methodology for revenue dependent infrastructure projects.