But are you ready for it?

Recently, I had the opportunity to attend a demonstration of some quantitative risk analysis software, and it was incredible! There is a lot of exciting potential for risk management using quantitative analysis – the most common risk tools like heat maps and probability/impact matrices give some insights about project risk but they mostly rely on subjective judgements from the project manager. The software I saw was much more procedural – it layered on top of Excel and allowed the user to quickly and easily run simulations and create statistical models for many different risk scenarios.

As an example of how quantitative risk could be applied, take a large construction company. This company expects to remodel 100 houses in a year wants to predict their costs for common materials like lumber and tiles. Prices for these goods can fluctuate throughout the year, and the company wants to model potential scenarios to get an idea of their expected costs. Using some simple parameters (standard unit price, low value of -10%, high value of +20%, etc.), quantitative risk software can run thousands of scenarios (known as a Monte Carlo simulation) to model a PERT distribution of potential costs for the building materials. This analysis can give the company the spread of confidence intervals and costs to make informed decisions about the quantities of supplies they should buy.

Sounds great, right? In theory, quantitative risk analysis is much more precise than qualitative analysis tools. But the issue with quantitative risk analysis isn’t simulation results, it’s how those results are represented to stakeholders. Qualitative risk analysis leads to a fairly simple assessment: “there is a 50% chance of the weather delaying our project, and here is the plan to address weather delays.” Most people, especially executives and project sponsors, can use this type of assessment to make decisions effectively without training.

Quantitative risk analysis is different because the results are represented probabilistically. This means that the results are displayed as defined range of the total spread of a simulation. In the construction example, you can use this method to say, “we want to be 80% confident that we can cover the cost of our materials over the course of a year. We’ll allocate the budget based on the 80th percentile of the Monte Carlo simulation, which equates to $X for materials for the year.”

Representing risk probabilistically is dramatically different from typical qualitative risk assessments, and many stakeholders on a project may not be prepared for it. This is an issue with education and training; most MBA programs have a statistics portion of their curriculum but rarely cover probabilistic modeling or interpreting such probabilistic results. On-the-job training programs also rarely cover this material, unless a quantitative risk management is already implemented and ingratiated into the culture of a business.

To realize the full benefit of a quantitative risk approach, it’s not enough for a business to buy the software. Organizations need to prepare their management chain; managers and executives need to be trained to interpret a quantitative risk analysis and make decisions accordingly for this approach to yield tangible business value.