Monte-Carlo Simulation What is Monte-Carlo Simulation? Monte Carlo methods are often used when simulating physical and mathematical systems. Because of their reliance on repeated computation and random or pseudo-random numbers, Monte Carlo methods are most suited to calculation by a computer. It is useful for modeling phenomena with significant uncertainty in inputs, such as the calculation of risk in business. The Monte-Carlo stimulation determines probabilities of possible outcomes by running thousands of automated scenario analyses. Limitation of Scenarios Method is that it does not explicitly consider the probability of occurrence of different scenarios. This can potentially lead to scenarios with low likelihoods being assigned too much weight in the decision-making process. Unlike Scenarios Method, Monte Carlo simulation considers random sampling of probability distribution functions as model inputs to produce hundreds or thousands of possible outcomes instead of a few discrete scenarios. The results provide probabilities of different outcomes occurring. For example, a comparison of a spreadsheet cost construction model run using traditional “what if” scenarios, and then run again with Monte Carlo simulation and Triangular probability distributions shows that the Monte Carlo analysis has a narrower range than the “what if” analysis. This is because the “what if” analysis gives equal weight to all scenarios.
How is Monte-Carlo Simulation performed? The uncertain inputs can be applied in terms of distribution models such as normal distribution, triangle situation, uniform distribution, and discrete distribution. It generates inputs randomly, and then performs a computation on each input. At the end, it aggregates the results into our final result. In an example 16.4 in the book, we use two of @ risk functions, RISKNORMAL, simulating a normally distributed demand, and RISKSIMTABLE, running the simulation several times. The inputs in the spreadsheets are in the range B4:B6. The inputs for the demand distribution in the range E5:E6, and the possible order quantities in the range G4:G8.
     Why is Monte-Carlo Simulation appropriate for managing uncertainty in complex situations?
As a result of the analyses, the histogram shows that there is a very high possibility that profit will reach approximately $375, but there is also a possibility that profit will be less or negative. In contrast, the histogram of profit when the order quantity is 250 is much more spread out. The distribution of the outcome is important it repeats random sampling to compute the results and emphasizes every possibility. |