Quantrix is an excellent tool to do business forecasting and planning. But planning often means deterministic extrapolation of the past. Unfortunately, the future has always been unpredictable, but with Covid, Climate Change and the Russian war, the world is changing in a disruptive way. Supply chains are at risk, inflation and raw material prices make cost unpredictable. A lot of people see the need to include volatility and risk in their planning process.
With this post, I want to share techniques how to build risk, uncertainty and scenario thinking into a corporate financial model in Quantrix. All uncertain inputs to the P&L, Balance Sheet and Cash Flow should be modeled as risk distributions. Using Monte-Carlo simulation, thousands of recalculations can be done to provide output distributions of all KPIs, which can be transformed with descriptive statistics into a management-friendly presentation to support decision processes.
We share with you a „Monte-Carlo-Simulation“ starter kit. It consists of a Function Plugin for easy use of some risk functions, and a simple script to handle Monte-Carlo-Iterations. It contains test demo of all Plugin functions. In the end, we present a simple didactic Monte-Carlo-Business case which explains how to build a Monte-Carlo Modell. This should you get running in building your own Simulation models in Quantrix.
I want thank all the people that supported me in building this demo kit, especially
- Dominic Etter, Group Controller at ALPIQ who developed their Monte-Carlo model with me.
- Vladimir Forfutdinov (firstname.lastname@example.org, Vladart LLC) He got me started in creating the Risk-Library-Plugin.
- All the cool people at Quantrix, how supported this project and helped me tremendously with their advice and suggestions how to improve my solution. Especially Lindsey Weber, who provided me with a first draft of a MC model and accompanied me during the entire work. And Ben Wake, which explained me the inner workings of Quantrix to build the model in an efficient way and provided solutions to several nasty problems and helped to increase performance.
I hope you enjoy starting to work with Monte-Carlo-Simulations. I would love to hear your feed-back on this Starter-kit, and am happy to answer your questions here in the forum. Please tag all posts related to this with the tag „Monte-Carlo-Simulation“.
P.S.: If you are interested in a demo if a real-world model, watch my Dimension 2022 presentation on youtube!