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1. Principles of Monte Carlo Simulation
Monte Carlo simulation can be useful in many situations and industries. Learn what it is and how it works in this lesson.
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Lesson Goal (00:08)
The goal of this lesson is to learn about the principles of Monte Carlo simulation.
When Monte Carlo Simulation is Useful (00:15)
Monte Carlo simulation can be used in any business situation where there is uncertainty over future outcomes. For example, when developing a product, we want to know if the new product will be profitable. There is uncertainty over the demand for the product, the development time of the product, and the costs associated with this development.
A simple method of dealing with this issue is to calculate profit in different scenarios, such as a best-case, worst-case, and base-case scenario. However, this method can’t account for every possible scenario, and it doesn’t tell us anything about how likely each scenario is.
Understanding Monte Carlo Simulation (01:22)
A simulation is a model of a possible outcome for a business process. A Monte Carlo simulation is a repeated simulation of a business process that is used to analyze all the possible outcomes of the process, and the probability of outcomes of interest.
When using Monte Carlo simulation, we simulate the problem a large number of times. This ensures that all the possible outcomes are likely to appear in the simulation. It also allows us to analyze an outcome of interest by determining how often it occurs in our large body of simulations.
Monte Carlo Simulation Examples (02:24)
This course considers three examples of Monte Carlo simulation. The first example analyzes the outcomes from betting on a roulette wheel. The second considers the length of a product development process. The third focuses on the potential profits from a new product.
In this course, we'll learn how to create a number of Monte Carlo Simulations in Excel.
In this lesson, we'll learn about the principles of Monte Carlo Simulations.
Monte Carlo Simulations are used in a variety of industries, from finance to science.
Monte Carlo Simulations can be used in any business context where there is uncertainty over future outcomes.
Later in this course, we'll consider a company called LoudCo. The company develops a range of tools, such as drills, saws, and axes. They are considering the development of a new product line, and want to know whether the new product will generate a profit.
There are many aspects of this project that are uncertain, such as the length of time the development will take, the cost associated with the development, and the potential demand for the new product. One possible solution to this uncertainty is to evaluate various possible scenarios for the project. For example, the business may be able to create best case, worst case, and base case projections for the new products demand.
However, it's unlikely that the company will be able to consider every possible scenario in this way. Also, this method doesn't help us identify the likelihood of each scenario. A Monte Carlo Simulation can help solve these problems.
A simulation of business processes is a model of a possible outcome for that process.
A Monte Carlo Simulation is a repeated simulation of a business process. It helps us analyze a business problem by running a large number of repeated simulations. We can then understand the problem by analyzing the trends and patterns present in these simulations. For example, LoudCo can repeatedly simulate the development of their new product to analyze the likely profit or loss from the development. By running a large number of simulations, we ensure that all the realistically possible outcomes will appear in our simulation.
We can also analyze the probability of some outcome of interest, by identifying the proportion of simulations where that outcome occurs.
For example, LoudCo can identify the probability that they'll new product will make a loss, by determining how many simulations involve the product making a profit. In this course, we'll consider three examples of Monte Carlo Simulations.
First, we'll introduce the concept of Monte Carlo Simulations with an example related to betting on a roulette wheel. We'll then create two Monte Carlo Simulations for LoudCo, focusing on the time taken to develop their new product, and the profit that new product can be expected to generate.
Now that we've learned how a Monte Carlo Simulation works, we're ready to start using it in Excel.
In the next lesson, we'll learn how to create a simulation of a single event in Excel.