Six Sigma and Business Analytics: Monte Carlo Simulation

Doing business is all about taking risks. Whether you’re deciding to become a full-time entrepreneur, open a new office, or invest in a company, you’re taking risks. However, not all risks are necessarily “risky”. Let’s explain. While you may be taking a risk by venturing into a new investment scheme, chances are you will analyze the actual risk before you begin. Likewise, most business risks are carefully thought and planned through. Yet, how can you accurately predict how risky your next business decision will be? Six Sigma is your go-to process improvement methodology. With a foundation in data analysis, Six Sigma proves to be a great tool in operating your organization. That’s why it’s no wonder that Six Sigma professionals use the Monte Carlo simulation for their problem-solving and risk assessment needs!

What is Monte Carlo Simulation? 

By definition, Monte Carlo simulation is a mathematical tool that assesses the likelihood of certain outcomes. By using problem-solving and risk assessment techniques, it approximates the risk of a particular result. This simulation uses a variety of data input and is ideal for most fields and industries. More importantly, Monte Carlo simulation provides you with insight into the most likely, least likely and an average outcome for your situation. When you have questions such as “Will this investment yield a high return?” or “How expensive with this project be?”, Monte Carlo can calculate approximate predictions.

How to Use Monte Carlo Simulation

Like most Six Sigma tools, Monte Carlo depends heavily on the data you provide. In most cases, more data is always better. With additional data and multiple variables, it’s easier for the simulation to provide you with precise estimations. When using this simulation tool, you’re building a model of possible outcomes. It will display ranges for the certain outcomes, also known as a probability distribution. Likewise, the simulation can run for any designated amount of time. For example, if you want to know how expensive your next project will be over the course of 18 months. Or, if you need to know your return on investment for every quarter for the next three years 

Six Sigma and Monte Carlo

Although Monte Carlo simulation is an ideal tool for most professionals, it is not bulletproof. Providing the wrong data, inaccurate variables, or unrealistic ranges will not offer the most accurate results. This is where Six Sigma comes into play. As a Six Sigma professional, you understand how to properly mine, sort, and analyze data sets. Likewise, you also have experience managing other Six Sigma employees who collect data for your project. When using Monte Carlo, you should use historical results to create the most realistic range to test. Likewise, comparing your simulation results to past experiences can help determine if you ran the program correctly.

It’s important to remember that Monte Carlo is purely a simulation and does not guarantee exact results. However, enrolling in our Six Sigma professional courses and training will prepare you for this and more business analytics tool in the future!

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