Monte Carlo simulation

Recently I read one of the good articles about Monte Carlo Simulation and today I am going to write something about this Method that can be used as a computerized technique for professional decision-making.

Since I’m not the subject matter expert in this method, I’m trying to express my views/understanding as a knowledge sharing about this technique to my fellow friends who can use it in their business decisions.

Though I’m not sure how far this can be used in Procurement decision-making however it seems helpful in business-level decision-making.

In day-to-day life, we have to take various decisions professionally as well as at a personal level. And in many situations, while taking decisions we must have cognizance of various outcomes of our decisions.

In everyday life, you have to check the pros and cons of every decision you take. Even if your decision to travel to any place is considered, you need to consider various scenarios, if you decide to travel by Train, is the destination fall on your train route? What if the train is delayed? if you decide to travel by bus and you might get stuck in traffic that may delay your journey to reach the destination. So this way you have to always look for the probability of an outcome that may become a hindrance in your plan to reach the goal.

Likewise in the Professional world many times it’s difficult to take decisions if the probable outcomes are more critical in decision-making. Monte Carlo Method is used professionally when the stakes are high, it’s essential to calculate, as thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a model used to predict the probability of a variety of outcomes, it’s a computerized technique used to generate models of possible outcomes and their probability distributions.?

This Method was invented by John von Neumann and Stanislaw Ulam during World War II to improve decision-making under uncertain conditions.

This method is used by data analysts to conduct advanced risk analysis, allowing them to better forecast what might happen in the future and make decisions accordingly.

It considers a range of possible outcomes and then calculates the probabilities of how likely each particular outcome will be realized.

So how does Monte Carlo simulation work, and what can it tell us??

Unlike a normal forecasting model, Monte Carlo Simulation predicts a set of outcomes based on an estimated range of values versus a set of fixed input values.

To run a Monte Carlo simulation, you’ll start with a mathematical model of your data such as a spreadsheet. Within your spreadsheet, you’ll have one or several outputs that you’re interested in. You’ll also have several inputs; these are variables that may impact your output variable.

In other words, a Monte Carlo Simulation builds a model of possible results by leveraging a probability distribution, such as a uniform or normal distribution, for any variable that has inherent uncertainty. It, then, recalculates the results over and over, each time using a different set of random numbers between the minimum and maximum values.?

If you’re looking at profit, relevant inputs might include the number of sale leads, total marketing spend, Production cost including machine cost, and employee salaries. If you knew the exact, definitive values of all your input variables, you would quite easily be able to calculate what profit that left with at the end.

However, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if you make 700,000 production units and hire twenty new employees on a salary of INR 50,000 each? What is the likelihood of this outcome? What will your profit be if you only make 20,000 production units and hire five new employees? And so on.

It does this by replacing all uncertain values with functions that generate random samples from distributions determined by you and then running a series of calculations and recalculations to produce models of all the possible outcomes and their probability distributions.?

Monte Carlo Simulations are also utilized for long-term predictions due to their accuracy. As the number of inputs increases, the number of forecasts also grows, allowing you to project outcomes farther out in time with more accuracy.

When a Monte Carlo Simulation is complete, it yields a range of possible outcomes with the probability of each result occurring.

This is probably very brief information about the Monte Carlo simulation and one can do deep dive to understand it if to use it in business decision-making.