As a cornerstone of optimal decision-making in many fields, from the prediction of financial success to the reliability of power sources, Monte Carlo simulations offer a way to transform risk analysis into a much simpler task. Creating probabilistic risk models with the Monte Carlo method can offer clear expectations – like whether a business will make money on a new venture or complete a construction project on time.
If you don't fully understand what these probabilistic simulations can do for you, keep reading.
In this article, discover the most potent Monte Carlo simulation examples that have appeared over the years to ensure a wide range of industries can operate at their full potential. Explore how different working environments benefit from its use – from engineering and energy to finance and healthcare. By the end, you should have a broader understanding of how to use the Monte Carlo method in your projects.
What is a Monte Carlo Game?
The Monte Carlo game is a simplification of a complicated system of determining the probabilities of events using random chance. For example, consider a game where you would roll a dice to see what outcome would occur. By playing such a game repeatedly, you can start to build stats and patterns, such as how often you roll a six followed by a two.
The Monte Carlo simulation technique uses this concept to map the idea onto a real-world problem. For example, if you’re launching a new product, you would want to calculate the probability of your product succeeding in the market. You could use the Monte Carlo method to calculate the range of possibilities of all uncertain variables that impact the end result. By running the simulation thousands of times with random numbers, like rolling a dice thousands of times, you’ll calculate the range of possible outcomes for your product launch.
Uncertain factors for a new product launch could include:
- Customer preferences
- Prevalence of competition
- Economic conditions
- Community outreach success likelihood
Together, these offer a range of probabilities, as opposed to static values, that can help you predict how likely a product is to get off the ground.
To accomplish this, you will need to engage with several different steps including:
- Creating an initial model of the situation at hand
- Simulating many different scenarios
- Analyze the results you collect from the situation
- Make adjustments to your plans and run the scenarios again
Such processes offer the highest chance of finding a solution that will lead to business success. Even NASA uses these simulations to plan space missions.
As you will see from the following Monte Carlo simulation examples, the results can be useful for organizations of any size and in any industry.
Practical Applications in Finance
The world of finance has many overlapping levels of complexity that can make most simulations challenging to prepare. Investopedia confirms that predicting the market's ups and downs perfectly may be impossible, but the Monte Carlo model helps you get close. It may not offer guarantees, but instead, Monte Carlo results are often an accurate predictor of probability.
As you run thousands of Monte Carlo simulations based on probability, you will receive results that define the most likely outcomes as a set of probabilities. You can then prepare for the results with the highest probability of success or based on your risk tolerance.
This strategic risk management allows financial planners to make more informed decisions that can save them money in both the short and long term. Companies can put resources in place to ensure failure is less likely to lead to catastrophe by planning for the best but also making better preparations for the worst.
Monte Carlo Simulation Examples in Finance
The Monte Carlo method is well-suited to assessing investment portfolios due to the volatility of such assets. With a set of resulting probabilistic outcomes related to different investment strategies, any investor can place their assets in locations most likely to give them their desired result.
If an investor can clearly see the range of probable returns alongside risk, they can properly mitigate or take strategic risks with data backing their decisions. For example, when debating whether to buy or sell an option, an investor can model the probability of its future growth or decline.
Another example of using Monte Carlo simulation in financial risk analysis to hedge against volatile exchange rates. As the value of major currencies can change at a moment's notice, it’s critical that the treasury planning process accounts for this variability in exchange rates.
Enhancing Engineering and Manufacturing Projects
Monte Carlo simulations are not limited to handling numerical or social predictions. This method can also help forecast the reliability of large physical systems.
If you understand the likelihood of the various parts of a mechanism to break down, such as an engine, you can start to model those variables with a range of probability for each part. You can even include how each failure could impact the rest of the system, giving additional nuance the further into the simulation you go.
The information provided by using the Monte Carlo method can help optimize complex engineering projects. Plus, when using powerful risk analysis software like @RISK, you can conduct sensitivity analysis to see which values have the greatest impact on the end result.
Simulations are also helpful when planning the manufacturing process itself. If one step in the creation process of a product is likely to cause bottlenecks, Monte Carlo simulations can offer information about which steps are the most likely to cause setbacks. You can then reinforce these weak points in the chain and ensure there are backup methods for resolving that step in the process.
A Monte Carlo simulation is also well-suited to minimizing waste by helping you discover when resource reallocation would be beneficial. You can then support different project initiatives by predicting both the efficiency of the whole system and its individual parts.
Medical Research in Pharma
Clinical trials are traditionally a risk-filled process where the wrong decision could be costly in both time and money. By applying the Monte Carlo method to clinical trial models, you can:
- Calculate the risk adjusted net present values (rNPVs)Â for preclinical and clinical drug candidates.
- Conduct sensitivity analysis to the dominant decision
- Determine the maximum cost of an initial proof-of concept (POC) study
- Investigate the best pathway for a novel drug expected to treat multiple diseases (indications)
Using the simulation results, you can optimize your trial’s design to improve efficiency and success rate.
Benefits of Using Stochastic Processes in Healthcare
Epidemiology often uses Monte Carlo simulations to model the spread of infectious diseases among population centers. They simulate how a disease might appear and move through a population under different conditions, such as:
- Varying rates of transmission
- Different levels of population immunity
- The results of different public health policies
- The impact of superspreader events
With the data from these models, policymakers can predict and plan for different responses. In doing so, they can make decisions to protect the largest percentage of the population.
During the COVID-19 pandemic, Monte Carlo simulations were a standard method for estimating the spread of the virus. By modeling a pandemic’s spread and health care surge capacity, hospitals could improve planning and take steps to ensure proper staffing, supply, and pharmaceutical needs. Researchers such as Amaro, J. E., & Orce, J. N. (2022) prove that such techniques are in use to learn how this infection may impact the world in the future.
Healthcare organizations need to ensure that everyone has what they need to combat a healthcare crisis or handle a disaster. As such, they need to ensure different areas have access to resources such as:
- ICU beds
- Ventilators
- Medical staff
- Pharmaceuticals
- Emergency shelters
- Military support in extreme cases
- Clean and safe food sources
Monte Carlo simulations can assist with decision-making to ensure adequate resource allocation. They can plan what might occur in circumstances where different equipment or people are apportioned to different areas. The lifesaving, data-driven decisions that result from running Monte Carlo simulations can offer greater preparedness to ensure the least impact on a region.
Inspired to Start Leveraging Monte Carlo Methods?
These Monte Carlo simulation examples are just the tip of the iceberg for how this technique can empower robust, data-driven decisions. With actionable insights and clear results, you can improve the likelihood of successful outcomes for all your research, projects, and plans.
Plus, Lumivero makes applying Monte Carlo simulation to your models easy with comprehensive tools like @RISK and DecisionTools Suite to help you start building your own probabilistic models – all within Microsoft Excel! With an easy-to-use interface and a reputation for accuracy, our decision-making tools can help you no matter your industry. Get in touch to learn more about how @RISK can benefit you moving forward.