SimDec Excel tutorial

Is an electric car really worth it?
Instead of guessing, let’s model it.

In this short video tutorial, Mariia Kozlova walks through a simple yet revealing valuation exercise: using Monte Carlo simulation and simulation decomposition (SimDec) in Excel to compare the total ownership cost of an electric vs. a gasoline car.

From Numbers to Meaning

The model is built in Excel, with pre-coded macros for running random sampling and visual decomposition. It allows the user to vary uncertain inputs such as:

• Car price
• Annual mileage
• Electricity price

Each simulation run calculates the cost difference (benefit) between electric and gasoline vehicles over five years.

The Monte Carlo part shows the spread of possible outcomes — the range of how much better (or worse) one car might be than the other.

Then, Simulation Decomposition (SimDec) breaks down this variation into interpretable parts, showing which input combinations produce which outcomes.

What It Does — and What It Doesn’t

SimDec is designed to visualize uncertainty structure and reveal how inputs jointly affect outcomes.
[It does not calculate global sensitivity indices. For that functionality try SimDec dashboard at simdec.io]

In this example, SimDec reveals that the car price dominates the result:
if the electric vehicle costs less than $40,000, it’s likely to outperform the gasoline car financially.
Mileage, while still uncertain, plays a smaller role once price variation and electricity cost are accounted for.

Try It Yourself

You can reproduce this experiment using the same Excel template from the video:
🧩 GitHub Template: https://github.com/Simulation-Decomposition/simdec-excel

And for the full academic background, see:
📘 Reference Paper:
Kozlova, M., & Yeomans, J. S. (2022). Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines. INFORMS Transactions on Education, 22(3), 147–159.
https://doi.org/10.1287/ited.2019.0240

In Short

This simple exercise demonstrates that SimDec turns a Monte Carlo model into an interpretable picture, where uncertainty, interactions, and trade-offs become visible at a glance.

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