Mark Brown

Mark is a Vice President of Sword Active Risk and was one of the founders of the Active Risk Manager software solution – the defacto standard in delivering objectives on critical capital projects. 

He is a member of the Chartered Accountants society of New Zealand having worked in government, banking, consultancy with Deloitte and running one of SunGards businesses in Europe for a risk based trading system. 

In the last 18 years he has helped many bluechip companies whose names are synonomous with quality project risk management build and deliver successful projects.

Synopsis: Thought Leadership - Using Artificial Intelligence in large project schedules to help Risk Managers identify risks normally hidden by the complexity of project schedule networks.

Large projects present specific problems to risk managers in that the ability to identify schedule risks and potentially resulting cost and technical performance risks is made almost impossible by the scale of the schedule and the complexity of the network relationships. So do we just focus just on critical path activities? Milestones? Or rebuild a “summary” schedule?  All current approaches have significant weaknesses. For a project schedule of more than 10,000 activities that becomes a daunting task and the schedule changes.. and let’s say it was a 500,000 line activity schedule?....

Is there a better way of understanding the schedule from a risk perspective? Can AI help support business decisions and performance and how? Will hidden aspects of risk that you simply cannot detect from the WBS network now become more transparent?