Monte Carlo Simulation with Python to predict the profit from launching a new product

Create Monte Carlo Simulations with Python

Monte Carlo simulations are useful for modeling uncertainty in various fields, such as finance, engineering, and science. This guide explains the basics of Monte Carlo simulations and how to implement them using Python.

What is Monte Carlo Simulation?

Monte Carlo simulation is a technique used to model the probability of different outcomes in processes with random variables. It repeatedly runs simulations using random values to predict a range of possible outcomes. The steps include:

1. Model uncertainty: Define probability distributions for variables.
2. Random sampling: Select random values from these distributions.
3. Simulate outcomes: Use the values to simulate the system’s behavior.
4. Analyze results: Repeat the process multiple times to understand the range of outcomes.

Python Implementation Example

Let’s consider a scenario where a company wants to predict the profit from launching a new product, using the following assumptions:

• Demand: Normally distributed with a mean of 10,000 units and a…