From an owner’s perspective, risk analysis means trying to determine that the amount of capital expenditure allocated to a venture is appropriate, i.e. the most cost-sensitive variable in our project is time.
We also have to take into consideration that CAPEX projects have a well-earned reputation for running over with regard to time. Often you have competing work, competing work phases and limited resources.
There’s also a huge degree of dependency between the sequence of design, procurement, fabrication, delivery, installation and then commission and close out. Such a highly sequential set of dependencies really puts the owner organisations at extreme schedule risk.
But what if we could use science itself to understand the risk and uncertainty of a project upfront and set our expectations closer to reality right from the beginning? Driven by today’s Artificial Intelligence, or AI, project risk analysis is bringing an entirely new layer of value and depth for owners.
The “Best Case Scenario” Trap
Many people are familiar with the traditional risk analysis process where we start off with what we call a deterministic forecast. We strip out any contingency that’s already built into that forecast because, ultimately, the percentage of contingency is what we’re trying to ascertain. We then model the risk through two variables; uncertainty and actual risks. We then do a risk analysis which amounts to running the project or simulating executing the project thousands and thousands of times. Then, out of that, come the risk results.
Unfortunately, traditional estimating and scheduling tends to generate a best-case scenario rather than a most-likely scenario. This is because a planner or scheduler will develop a schedule forecast, but very rarely will that forecast include some of the potential unknowns in the form of risk events that could potentially happen during execution. So, the plan that is created assumes uneventful, perfectly executed events. And as we all know, best-case scenarios very rarely happen.
How AI can Help
One huge step forward with regard to AI involves eliminating some of the statistical complexity that legacy risk analysis carries with it. Previously, we would interview team members and ask them for three-point estimates, or three-point distributions of best, most-likely and worst-case scenarios. Well, that’s actually very difficult to extract out of a team member. But with AI, we have large quantities of historical data at our fingertips. The benefit there is now we’re not just limited to three-point estimates. For example, if we have ten historical projects in our AI knowledge library, we can end up with an input distribution that has ten points, not three points. So, it becomes much more accurate.
Another challenge with traditional risk analysis has been to accurately capture those inputs. With AI, we have the luxury of leveraging historical project information throughout the project life cycle. Computers can now review prior project schedules, cost estimates and even previously generated risk registers, and start making suggestions back to the planner and the scheduler during the planning process. This eliminates the need to build a plan and then reactively come back and throw contingency at it later.
Overall with AI, we’ve reduced the overhead and the complexity of the development of a risk register by allowing the computer to make suggestions based on historical risk events that have occurred before that impact schedule and cost. That’s an incredible step forward because now we can start to build our risk register on human expertise and on the suggestions of AI, calibrating a risk register based on the best of machine and human input.
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