Monte Carlo analysis has the advantage of increasing the reliability of project budget estimates. The method shows how parameters behave depending on the choice of possible extreme decisions, with all their consequences. The simulation provides decision makers with a range of outcomes and probabilities that will occur after each choice of action. Using the input data, a clear graphical display of the results can also be presented.
Not performing a Monte Carlo analysis at the start of a project does not doom it to failure. Among the Project Managers interviewed, many indicate that they use deterministic modelling instead of the method in philippine cellphone number code question. It involves selecting a risk and developing a step-by-step solution for each point. The advantage of Monte Carlo over the deterministic approach is the possibility of translating risks into numbers and observing the probability distribution for each of the scenarios analysed.
Monte Carlo can be not only a way to save a project, but also a method for achieving success and making key decisions. By becoming familiar with endogenous factors, such as team strength, or exogenous factors, such as the possibility of supply chain disruptions, you can develop the project's resilience to the risks involved and achieve its stated objectives.
Monte Carlo method step by step
In the first step, we need to determine the purpose of our analysis: whether we want to focus solely on the development of the project schedule or whether it is also worthwhile to include financial aspects in the project. Next, we need to develop a risk model based on the existing baseline and fill it with the necessary values. It is important that the data reflects all relevant risks, including both threats and opportunities. One disadvantage of Monte Carlo simulation is the possibility of obtaining a false result by supplementing the model with inaccurate or faulty data.
The next step is to repeat the simulation many times to work out all possible scenarios. The advantage of iterating the model is the ability to observe the real risk factors, the possible outcomes and the probability of achieving the set objectives.
During the implementation of a quantitative survey with and without risk responses, we can detect logical errors and incorrectly entered data. A second version of the model must then be created to include the effect of the agreed responses. Comparing the two developed models will allow us to observe how the planned actions affect the overall risk exposure and whether our responses are adequate.
Once we have the results, we can proceed to prioritize the actions and plan the critical path of the project. By carefully analyzing the probability of the scenarios, we can also carefully develop a plan with the team to deal with any of the risks.