INTRODUCTION
‘Simulation is defined as the process of creating a model (i.e., an abstract representation or facsimile) of an existing or proposed system (e.g., a project, a business, a mine, a watershed, a forest, the organs in your body) in order to identify and understand those factors which control the system and/or to predict (forecast) the future behavior of the system. (Eckhardt, 1987).’
‘Monte carlo simulation , or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management , cost and other forecasting models. (What is Monte Carlo Simulation, n.d. , para.1 )’. ‘Monte Carlo simulation was named after city in Monaco (famous for its casino) where games of chance (example, …show more content…
These assumptions might be the cost of construction project or investment return on a portfolio. Because these are projections into the future , the best is to estimate the expected value. Estimates can be based on historical data, or past experience or expertise in field. An estimate contains uncertainty and risk because it’s an unknown value projected in future.
Let’s take the example of construction project. The project contains 3 tasks. How long will take to finish the tasks of the project. You can estimate the absolute maximum time it might take or the minimum time in the best case.
‘By using a range of possible values, instead of single guess, you can create a more realistic picture of what might happen in the future. When a model is based on ranges of estimates , the output of the model will also be a range.’ (‘What is Monte Carlo Simulation’ , n.d. , para.4 ). Moreover, by using range of values, we are understanding more the risk and uncertainty in the forecasted model. Back to our example, when ranges of maximum and minimum estimates are given to each of the tasks needed to complete the project, then we can use those values to estimate the maximum and minimum time of the whole project. Monte carlo simulation gives you how likely the resulting outcome would be based on the range of estimates created but …show more content…
Second, out of the 500 trials, the total time of 14 months or less has shown 34% of the cases. Furthermore, results show that 79% chance could be 15 months or less for the project to be completed. Based on this information, we might make different choices when planning the project. Such information might have an impact on our financing, insurance, permits and hiring needs.
Figure 1: Probability of completion within specified time (Months) (retrieved from www.riskAMP.com)
Conclusion
The simulation will only be as good as the estimates the manager makes. ‘It’s important to remember that the simulation only represents probabilities and not certainty. Nevertheless, Monte Carlo simulation can be a valuable tool when forecasting an unknown future. (What is Monte Carlo Simulation, n.d. , para.14 )’.
References • RiskAMP is a Monte Carlo Simulation engine that’s works with Microsoft Excel. Retrieved from http://www.riskAMP.com).
• Eckhardt, Roger (1987). Stan Ulam, John von Neumann, and the Monte Carlo method, Los Alamos Science, Special Issue (15),
‘Simulation is defined as the process of creating a model (i.e., an abstract representation or facsimile) of an existing or proposed system (e.g., a project, a business, a mine, a watershed, a forest, the organs in your body) in order to identify and understand those factors which control the system and/or to predict (forecast) the future behavior of the system. (Eckhardt, 1987).’
‘Monte carlo simulation , or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management , cost and other forecasting models. (What is Monte Carlo Simulation, n.d. , para.1 )’. ‘Monte Carlo simulation was named after city in Monaco (famous for its casino) where games of chance (example, …show more content…
These assumptions might be the cost of construction project or investment return on a portfolio. Because these are projections into the future , the best is to estimate the expected value. Estimates can be based on historical data, or past experience or expertise in field. An estimate contains uncertainty and risk because it’s an unknown value projected in future.
Let’s take the example of construction project. The project contains 3 tasks. How long will take to finish the tasks of the project. You can estimate the absolute maximum time it might take or the minimum time in the best case.
‘By using a range of possible values, instead of single guess, you can create a more realistic picture of what might happen in the future. When a model is based on ranges of estimates , the output of the model will also be a range.’ (‘What is Monte Carlo Simulation’ , n.d. , para.4 ). Moreover, by using range of values, we are understanding more the risk and uncertainty in the forecasted model. Back to our example, when ranges of maximum and minimum estimates are given to each of the tasks needed to complete the project, then we can use those values to estimate the maximum and minimum time of the whole project. Monte carlo simulation gives you how likely the resulting outcome would be based on the range of estimates created but …show more content…
Second, out of the 500 trials, the total time of 14 months or less has shown 34% of the cases. Furthermore, results show that 79% chance could be 15 months or less for the project to be completed. Based on this information, we might make different choices when planning the project. Such information might have an impact on our financing, insurance, permits and hiring needs.
Figure 1: Probability of completion within specified time (Months) (retrieved from www.riskAMP.com)
Conclusion
The simulation will only be as good as the estimates the manager makes. ‘It’s important to remember that the simulation only represents probabilities and not certainty. Nevertheless, Monte Carlo simulation can be a valuable tool when forecasting an unknown future. (What is Monte Carlo Simulation, n.d. , para.14 )’.
References • RiskAMP is a Monte Carlo Simulation engine that’s works with Microsoft Excel. Retrieved from http://www.riskAMP.com).
• Eckhardt, Roger (1987). Stan Ulam, John von Neumann, and the Monte Carlo method, Los Alamos Science, Special Issue (15),