This project provides a comprehensive Monte Carlo simulation for analyzing the Net Present Value (NPV) of AI implementation initiatives. The analysis is built using Python with interactive controls and advanced statistical modeling.
Built with marimo-scipy-utils, monaco-dict-utils, and beta-pert-dist-scipy
- Python 3.13+
- uv package manager
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Clone or download this repository
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Navigate to the project directory:
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Install dependencies using uv:
uv sync
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Activate the virtual environment:
source .venv/bin/activate # On macOS/Linux # or .venv\Scripts\activate # On Windows
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Start the marimo notebook:
marimo edit ai_roi_mcm_npv.py
The interactive analysis will open in your web browser. Adjust the parameter sliders to see how different assumptions affect the NPV distribution and risk profile.
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NPV Model: Based on Excel financial model with 4 benefit categories:
- Time savings benefits from automation
- Quality improvements through bug reduction
- Product delivery acceleration
- Employee retention improvements
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Interactive Controls: Real-time parameter adjustment through sliders:
- Hours saved per employee per week
- Number of employees
- Fully-loaded hourly rate
- Bug reduction percentage
- Discount rate
- Number of Monte Carlo simulations
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Monte Carlo Simulation: Uses appropriate probability distributions for uncertainty modeling with thousands of simulation runs
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Comprehensive Visualization:
- NPV distribution histogram with mean and break-even lines
- Annual benefits breakdown
- Risk assessment pie chart showing probability of positive NPV
- Correlation analysis between benefit components
- Statistical summary tables with percentiles
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Risk Analysis:
- Probability of positive NPV calculation
- 90% confidence intervals
- Visual risk assessment tools