Why Timing is of the Essence when Analyzing Risk

on August 13, 2014 at 10:00 AM

The North Anna, Virgina, #1 and #2 nucle

One of the most common flaws of risk analysis is a lack of understanding and consideration of time, or more simply, timing.

Time waits for no one. We always understood this concept, but could never quite prove it scientifically until the 19th century. It was mankind’s desire to travel greater distances at greater speeds, and engineers studying the efficiencies of steam-powered engines, which helped form the Second Law of Thermodynamics, and finally prove that time is irreversible.

This phenomenon of nature is the bane of any project manager today. Further still, if not properly identified, managed and understood, it threatens future financing and confidence in our profession’s ability to deliver economically. Too often, investors and project managers accept traditional risk assessments laid forth in feasibility studies, information memoranda or investment proposals. Not only can these be superficial, they often can be fundamentally flawed.


The starting point of a traditional risk management process is a single-point estimate. Each individual risk in the project is identified. The potential impact of the risk is rated – normally on time and cost, and generally, as a product of its likelihood of occurrence and the effect it will have if it does happen. Even though the impact is actually a probability distribution of some form or another, this generally gets replaced with a single value (“if it does happen, then it’ll cost at least $1 million, but not more than $100 million, so let’s use an average of $50 million.”)

Let’s look at a 2 x 300 megawatt thermal power station. The team may identify that the failure of a generator unit during startup has a 1 in 1,000 chance of occurring. If it fails, then that’ll negatively impact the project by an average of $50 million due to the costs to repair it and the associated delays in producing electricity. The net result of this risk shown in the risk analysis is $50,000 (i.e., 0.001 x 50,000,000).

This is wrong. The total risk may be more than the sum of the parts.

Risks should not be viewed in isolation. Often correlations between individual risks are only considered (if at all) in an additive way.

So, at our thermal power station, a simple risk analysis would state the combined risk of both generators failing at $100,000 (i.e., 2 x $50,000). This most likely underestimates the actual risk to the project outcome.

The failures of the two generators probably share a common underlying cause. They are correlated. For example, if one generator unit fails because the insulating material used can’t take the heat, or the assembly team didn’t follow the correct bolt tightening sequence, or any one of thousands of reasons, if one fails during startup, then the likelihood that the other will also fail is no longer 1 in 1,000 but a lot closer to 100 percent.


The way projects are analyzed for risk is a lot more complex and sophisticated than the scenario above, of course. Advanced simulation techniques such as the Monte Carlo analysis are at our disposal.

These models, however, can still lead to serious flaws when trying to predict outcomes, as anyone who has frequented the halls of Monte Carlo or Las Vegas will testify. It is critical that the underlying causes of risks and the interrelationships between them must be fully understood.

There is an inherent danger in relying on averages, and when it is assumed that losses on the downside of one risk are offset by gains on the upside of another. This is the gamblers’ fallacy. There must be a positive correlation between events to affect the subsequent probability of the next event occurring.

On the simplest level, this applies to sequence. In all infrastructure projects, there are activities that can’t start (or finish) until two or more contributing activities have finished. Why do projects continue to fall behind schedule? Add in more and more parallel activities and merge points (typical as the project nears completion), and plain old common sense (as well as probability theory) tells you that the early-start situation is less likely to happen.


Let’s assume that the risk estimate calculated overcomes these common stumbling blocks and incorporates a probability distribution of the possibility that a risk may occur. This is a lot better than basic additive and single-point estimates, but it is missing out on one crucial consideration. Most risk assessments rarely consider when the risk may happen. An accurate risk assessment model must capture how the risk probability also varies over the life of the infrastructure asset or its financing.

Seasonal changes neatly explain this. The risk of flooding is intuitively higher during the rainy season. Likewise, the volume of work being carried out at any moment in time also influences risk as do the risks stemming from organizational complexities through the course of the project.

In effect, the shape of the probability distribution changes as the project progresses. What’s more, the impact of this risk also changes, and, generalizing, risks tend to have greater impact as the project nears completion.

Conceptually, we need to include a fourth dimension – time. Only now can our financial modeling of the loan or acquisition be better placed to fully capture the quantitative evaluation of the likelihood of the risk and its effect. This applies both in terms of its direct cost and of its impact on the timing of the cost or revenue flows.

Mathematical modeling approaches such as Probability Management, along with interactive simulation software, are available to provide us with the tools we need to properly capture the uncertainties in each project activity and asset element as they change throughout the entire asset lifecycle. We can model the impacts of these on the investors’ probable returns in the infrastructure asset.


We are living on a planet experiencing unprecedented levels of urban population growth. The demand for major water, energy and other infrastructure has never been greater. Yet, whether replacing aging infrastructure or building from scratch, we are facing an uphill battle to just, at best, keep pace with what we require as a modern society.

The key to delivering is money. The investment community needs project managers to help navigate risk and present investment opportunities that will more accurately deliver projected returns on investments. On the other side of the equation, the infrastructure investment community needs to recognize that there is a problem with the “risk management” status quo.

Even if, as project managers, we can’t halt the progress of time, we can at least better understand and estimate how it impacts the successful delivery of our project and how this impact changes over time. In a world of dwindling trust in financial models, this is one way we can, as individuals, increase financial confidence and help deliver more critical human infrastructure on budget and, yes, on time.

Published originally on Black & Veatch Solutions.