The Transpennine Route upgrade is a complex, multi-billion pound programme comprising numerous interdependent rail improvement projects across the North of England. Effective management of cost, schedule, and risk across the entire programme portfolio is critical for on-time delivery. This presentation will highlight how cutting-edge artificial intelligence is being applied to quantify and act on delay risk – reducing or avoiding project delay.

 

Specifically, technology platforms can leverage advanced machine learning algorithms to analyse disparate data sources, identify trends and patterns, model uncertainties and predict future performance of any construction project. This enables proactive risk management by flagging issues early, before they escalate. The AI models also allow for testing of "what-if" scenarios to optimise the programme. 

 

This talk will explore how AI can reliably quantify delay risk and provide actionable insights to project teams, including the potential impact this could have with insurers and project financiers.