Risk simulation is becoming popular but most risk simulation tools and the ways how they are used miss some important functionalities that make the results of this simulation unreliable.
Monte Carlo Simulation – Myth or Reality?
Last year, I have organised a poll on LinkedIn to understand what project practitioners think about Monte Carlo Risk Simulation:
The Monte Carlo Simulation Method is the best method for quantitative project risk analysis: Myth or Reality?
The poll had a lot of attention, and many projects and risk consultants shared their opinions.
Based on discussions, comments and the final result we can make some conclusions:
- Monte Carlo Risk Simulation (MCS) is the most recognised quantitative project risk analysis method;
- There is no common acceptance of this method across project practitioners;
- Opinion about Monte Carlo Simulation is mostly based on perception rather than knowledge;
- Majority of planners and risk consultants are mostly not aware of missing critical functionalities in Risk Simulation tools.
The popularity of MCS in recent years is primarily driven by companies that promote their own Monte Carlo Simulation software or Quantitative Risk Analysis (QRA) training. Unfortunately, well too often they measled project practitioners by telling them that it is easy to apply the method to get a reliable result. This is how one leading American consulting company that sells Quantitative Risk Analysis training attracts their clients:
“For many, Quantitative Risk Analysis (QRA) is a complex secretive technique, which relies on smoke, mirrors and mathematical trickery. The aim of this webinar is to draw back the curtain and show that QRAs are not that complex and by learning a few basic steps you can apply QRAs to any project to aid in their successful delivery.”
Demonstration of Monte Carlo principles based on the probability distribution of two dices may be good for a teenager, but projects are much more complex and deep knowledge is required to understand how to apply MCS correctly and which tool could be used to get reliable results.
Based on my research I found that different Monte Carlo Risk Simulation challenges are explained in conference presentations, blogs, White Papers and books but there is no single source where all challenges are collected or explained. I have decided to collect them and present the result at the Project Control Expo conference. 45 minutes is sufficient to cover only some key challenges at a high level. Fortunately, I am not limited by time and space on the Salute Enterprises blog and we could discuss each challenge in detail.
I am going to write a series of “Monte Carlo Simulation Challenges” posts, and discuss them on LinkedIn. If you are aware of any good source that explains such challenges please share them and join discussions on LinkedIn.