Cost-Estimating Studies Comparison - Case Study Example

This paper aims to discuss the cost-estimating case studies and compare them based on how they analyze risks. The case studies on Hydroelectric development and Irrigation scheme were both visited based on the project risk management guidelines and principles. It is important to identify risks especially their effect on cost estimation as a project is planned. The results would help the project board on determining if the project would proceed or should it be re-planned. Risks, may it be directly or indirectly affecting the project, need to be considered. The risks have an effect on the elements of the project and once should consider factors that may delay a project or may affect its cost, so that a contingency plan may be made. All of these will aid an organization in defining what will be the scope of the project and the time and cost involved in its completion.

Identifying and Quantifying Risks

Case study 1: Hydroelectric Development is better in identifying the risks since it listed the risk involved in each element, this makes it easier to see the possible risks and it is even grouped depending on what will be affected like quantity, unit cost, schedule, or global (overall). This is found in Table 20.3 - Risks for the hydroelectric project (John Wiley & Sons, 2005)

Case study 2: Irrigation scheme, on the other hand, is better in quantifying risks because it is able to assess the overall effect of risks to the elements, meaning, it balances each risk based on the strength of another risk or weakness of the other.  Each risk may have an effect on the other, and considering its combined effect is more accurate in quantifying the risk involved than by considering individual risk for each element. Dependence and correlation are also computed for the 2nd case study. As defined in Wikipedia:

Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example there is a causal relationship because extreme weather causes people to use more electricity for heating or cooling; however, statistical dependence is not sufficient to demonstrate the presence of such a causal relationship.

Overall, it is easier to follow the method used for Hydroelectric development because as mentioned above, it listed all risks involved for each element. The analysis is simple and straightforward, that all risks having both direct and indirect effects to elements were listed, one may also drill down on the sub-elements. Distributions are made individually which makes it simpler in visualizing the scenario and is just combined in the end.