Profit Forecasting with Monte Carlo Simulation

This project uses Monte Carlo simulation to estimate the expected annual profit and evaluate the financial risk for a business under uncertainty. It models key variables such as sales volume, unit pricing, and variable costs using probability distributions.

🔍 Objective

To simulate a range of financial outcomes and derive:

• Expected annual profit
• Risk assessment using the 2.5th percentile profit (Value at Risk)

🧮 Methodology

The simulation is based on the following assumptions:

• Sales volume is either 30,000 or 70,000 units with equal probability.
• Unit price depends on sales volume ($20 for low sales, $15 for high sales).
• Variable costs per unit follow a discrete distribution with values [$10, $11, $12] and probabilities [0.2, 0.6, 0.2].
• Fixed costs are constant at $20,000.

Using 1,000,000 iterations, the model estimates:

• Expected annual profit
• Profit at the 2.5th percentile (indicating downside risk)

📁 Project Structure

profit-simulation/
├── simulation.ipynb        # Jupyter notebook with simulation logic
├── README.md               # Project description and overview
├── requirements.txt        # Required packages

⚙️ Requirements

• numpy

Install with:

pip install -r requirements.txt

📈 Output

The simulation prints:

• Expected annual profit: e.g., $810,000
• 2.5th percentile profit: e.g., $290,000 (risk threshold)

🧠 Insight

This model helps business decision-makers understand variability in profits and prepare for worst-case scenarios using data-driven simulations.