This exercise showed that by leveraging past data, cost savings of up to £30m could have been achieved on the Great Western Main Line project alone. This was primarily achieved by flagging unknown risks to the project team – those that are invisible to the human eye due to the size and complexity of the project data – allowing them to mitigate those risks before they occur at significantly lower cost than if they are missed or ignored.
The technology works by learning from patterns in historical project performance. Put simply, the algorithm learns by comparing what was planned against what actually happened on a project at an individual activity level. This facilitates transparency and a shared, improved view of risk between project partners.
Alastair Forbes, Network Rail’s programme director (affordability) said: “By championing innovation and using forward-thinking technologies, we can deliver efficiencies in the way we plan and carry out rail upgrade and maintenance projects. It also has the benefit of reducing the risk of project overruns, which means in turn we can improve reliability for passengers.”
Dev Amratia, CEO and co-founder of nPlan, said: “Network Rail is amongst the largest infrastructure operators in Europe, and adopting technology to forecast and assure projects can lead to better outcomes for all of Britain’s rail industry, from contractors to passengers. I look forward to significantly delayed construction projects, and the disruption that causes for passengers, becoming a thing of the past, with our railways becoming safer and more resilient.”