Validity and Reliability
Validity and Reliability
The Monte Carlo simulation is used as a method that is used in projects to estimate the risk and uncertainty attached. In construction projects, engineers need to measure the time and the cost involved. These measurements are based on prior experience of the project manager. The time and the value of the project are estimated on the basis of certain assumptions. Monte Carlo simulation works in a way that a random number is selected for the each task. The model is then made which is based on the random numbers. The result that is generated from the model is stored and the process is repeated several times.
A normal simulation done through the Monte Carlo method calculates thousands of values. This results in the large number of values which are generated through the random number of inputs. The results are in the form of numbers which are the assumption on which different probabilities are measured. This measure the chances of getting various result in a model (Taylor & Francis, 1995). The information which the model yields is used to calculate the financing needed for the construction project, worker that need to be hired, insurance etc. Validity and Reliability: The reliability of the simulation is limited to the extent of its estimation.
If the variables are estimated accurately then the simulation gives the best measurement of the variables. Moreover, it can be argued that it is a reliable method for estimation of risk in project because of its quantitative management techniques. The Monte Carlo simulation is used by the project manager to fit in every possible circumstance associated with a risk and to calculate the probability of success associated with that risk. It is also used for the purpose of designing a project, which is done with help of different variable used as inputs (Loosemore & Uher, 2003).
The strength of the simulation lies on the fundamental fact that the estimate of the variable must be accurate for the best result needed. Moreover the extent of its measurement of the probability is surprising. It can calculate the success rate of the project before the starting the project. This is an advantage to the engineers as they can see the future of the project. The limitations, however that affect its proper usage include the difficulty to find the hardware and the software that is needed to perform the simulations (Brenda, 2003).
Then the engineers and the project managers involved are hesitant in using the simulation because they find it difficult to deal with statistical methods. Then many critics to the Monte Carlo simulation technique suggest that the method can yield the time schedule of the project that must be kept in reserve as well as the monetary aspect that must be stored as a reserve which can be disadvantageous to the cause of the project (Kwak & Ingall, 2007). Another reason that acts as a barrier for the use of this simulation is common construction project is that the simulation uses multiple parameters thereby forming a complex model.
It does not emphasize in the use of the single parameter which is used is most of the cases. Then in many cases the results that are generated through the simulation are perceived skeptical by the users, because of the psychological impact on the decision making. And finally, while making a model it is very difficult to define the connection between the activities which can never be estimated. So an approximation is used in the model to simplify the process. These approximations are uncertain and can at time be wrong.
When the approximations are wrong the can affect the interval and manipulate the result of the simulation (Brenda, 2003). Conclusion: Monte Carlo Simulation method is a very supportive tool for the purpose of making engineering projects. The reason is that the complexity of the simulation can manage different variable affecting the project and can present an analysis of the probability of success associated with the project. Reference: 1. McCabe, Brenda. (2003). Monte Carlo simulation for schedule risk. 2. Kwak, Young H. , & Ingall, Lisa. (2007). Exploring monte carlo simulation applications for project management.
Risk Management , 9. Retrieved from http://home. gwu. edu/~kwak/Monte_Carlo_Kwak_Ingall. pdf 3. Taylo, , & Francis, . (1995). Construction management and economics. E. & F. N. Spon. 4. Hinze, Jimmie. (1998). Construction planning and scheduling. Prentice Hall. 5. Flanagan, Roger, & George , Norman. (1993). Risk management and construction. Wiley-Blackwell. 6. Landau, David P. , & Binder, Kurt. (2005). A Guide to monte carlo simulations in statistical physics. Cambridge University Press. 7. Loosemore, Martin, & Uher, Thomas E. (2003). Essentials of construction project management. UNSW Press.