The film Oppenheimer captured the public’s imagination with the story of the Manhattan Project, the top-secret mission by mathematicians and physicists to develop the first atomic bomb. But did you know that some of the same mathematical brilliance that came from the Manhattan Project is also used by business analysts today?
One of the lesser-known achievements of the Manhattan Project was a technique called Monte Carlo simulation. This has become a powerful tool for decision intelligence that can help your team manage risk and improve your business forecasting.
Let’s take a closer look at the history of Monte Carlo simulation and see why it’s become so useful for business risk analysis.
History of Monte Carlo Simulation: Arising from the Manhattan Project
The scientists and mathematicians of the Manhattan Project were trying to do what had never been accomplished before, pushing the field of physics to its limits. They were engaged in a high-stakes race and operating in an environment of extreme uncertainty. Humanity’s future and the fate of the world hung in the balance. The Manhattan Project team needed a new way to estimate possible outcomes and understand the risks and rewards of various courses of action.
In 1946, a team of Manhattan Project scientists named Nicholas Metropolis, John von Neumann, and Stanislaw Ulam, developed what is now known as Monte Carlo simulation. Their team was attempting to create a “neutron diffusion model” to help predict what would happen in a chain reaction in highly enriched uranium. No one knew exactly what the real-life result of this chain reaction would look like, and the consequences of getting it wrong could be catastrophic.
Mathematical models and simulations were crucial to the Manhattan Project, because it often wasn’t possible to do real-life physical experiments. The nuclear materials needed to make atomic weapons were rare, costly, and time-consuming to source. Computer simulations, often performed by workers with the job title of “Computer,” most of whom were women, helped save time and resources by identifying and predicting successful weapon designs.
The team quickly realized that their model was too challenging to be solved by algebraic equations. They had to try using numerical methods instead – basically, a brute force approach of plugging lots of different numbers into their equations and finding the result. But the early punch card electronic computers of the time were not able to help: the problem had too many dimensions, and it took too long to plug in numbers for all the possible dimensions.
They needed a mathematical approach that would help them account for a high level of uncertain variables, not just hard numbers. The team devised Monte Carlo methods by plugging randomly chosen numbers into the equations. These methods allowed the scientists to cover the multidimensionality of the problem, without the limits of numerical methods. With the help of early computers like ENIAC and MANIAC, the team used Monte Carlo methods to obtain more accurate predictions.
How Monte Carlo Simulation Helps Manage Business Risk
Monte Carlo simulation lets people play “what if” with a large number of random, repeated samplings to discover a range of possible outcomes. For example, what if you wanted to know, “What are the chances of rolling a 7 at the craps table?” Monte Carlo simulation lets you simulate what might happen across thousands or millions of rolls of the dice.
Although it’s named after the famous casino, Monte Carlo simulation is not about gambling. It’s about visualizing the possible outcomes and weighing the risks and rewards of different decisions or courses of action. The scientists of the Manhattan Project were limited by the computing power of the era; ENIAC and MANIAC could only test a few thousand combinations of numbers. Today, business analysts have access to much more powerful computers that can run millions of possible combinations.
The stakes of business forecasting and decision intelligence are not as high as ending World War II or trying to stay one step ahead in a Cold War arms race. But the risk analysis problems that today’s business leaders face are often mathematically as difficult as the early process of designing an atomic bomb.
Nonetheless, your organization is operating in an environment of uncertainty while pursuing informed decisions concerning the allocation of scarce resources, all in the race to obtain the best possible outcome. Today’s business models for allocating funds to capital projects, evaluating competitor actions and promotions, managing production processes, or handling sales responses to pricing, are often too complicated to solve with algebraic equations.
Monte Carlo simulation is used in various industries and fields, such as pharmaceuticals, mining and minerals, aerospace, finance, insurance, construction, healthcare, energy & utilities, and more. It allows business decision-makers to play “what if” with uncertain inputs, evaluate risks and rewards, and determine the likelihood of various outcomes.
Here are just a few examples of how Monte Carlo simulation works for business analysts and business leaders:
- Identifying the likelihood of successful clinical trials for a newly developed drug.
- Choosing the highest-potential locations to explore for new sources of petroleum.
- Simulating future commodity prices, exchange rates, or interest rates based on changing market conditions.
- Forecasting the failure rate for machinery under different kinds of weather conditions.
- Showing how likely a project is to stay on schedule and on budget, depending on risk factors.
- Predicting customer acquisition costs, new customer subscription rates, and determining advertising budgets.
- Showing the expected impact of raising subscription prices on overall revenue and customer retention.
The legacy of the Manhattan Project lives on in today’s decision intelligence tools. Monte Carlo methods are helping business leaders solve challenging models, producing better informed decisions and improved outcomes for organizations across all industries – with a level of ease and speed that the Manhattan Project researchers would envy.
How Monte Carlo Methods Take Decision Intelligence to the Next Level
In the early days of Monte Carlo simulation, the Manhattan Project team was limited to punch card computers that could only handle a few thousand combinations of numbers. But today, every business leader has easy access to Excel add-in tools that are more powerful than those pioneering researchers could have dreamed.
Today, it’s easy to build a “what if” model on a spreadsheet, plug in numbers, and see the results quickly. But sometimes, business models are so challenging, with so many dimensions, that a simple “what if” spreadsheet model cannot cover the full range of possible outcomes. If your model’s actual outcome is falling beyond the range of possible results, you need a higher level of decision intelligence.
Fortunately, Monte Carlo methods are at your fingertips as part of Analytic Solver®, the all-in-one tool for optimization, simulation, and machine learning. Powerful, full-featured Monte Carlo capabilities are built into the Analytic Solver software. Our easy-to-use Monte Carlo methods enable you to cover the many dimensions of your business model, calculate a wide range of outcomes, and present the results in colorful, vivid charts and graphs, as well as hard numbers for key statistics. Analytic Solver also offers compatibility to push your simulation models to Power BI and Tableau.
Monte Carlo methods helped the world’s most brilliant scientists manage risk while building humanity’s most powerful weapons. These powerful mathematical simulation tools can also manage business risk and improve decision intelligence for your organization.
Want to see how the Analytic Solver® Monte Carlo simulation capability can give you a clearer picture of risk analysis and forecasting? You can try it for free. Request your Free 15-Day Trial!
If you’re still trying to decide which license is the best fit for your business analyst or team, don’t worry: the Frontline Solvers team will contact you within 48 hours of your request. We’ll discuss your needs and make sure you have the tools, examples, and support to start using Monte Carlo Simulation to conquer risk and uncertainty in your business.