Machine learning identifies £30m saving for Network Rail

A machine learning risk analysis tool has identified £30m of savings that Network Rail could have made on the Great Western Main Line.

Network Rail has tested nPlan’s system, which uses machine learning to identify patterns in historical project performance and produce accurate cost and time forecasts, on the Great Western Main Line and the Salisbury to Exeter signalling projects.

The theoretical £30m saving on the former project 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," according to Network Rail, allowing them to mitigate those risks before they occur at significantly lower cost than if they are missed or ignored.

Following this successful trial, nPlan and Network Rail will now embark on the next phase of deployment, rolling out the software on 40 projects before scaling up on all Network Rail projects by the middle of next year.

Network Rail said that nPlan’s technology will help to increase prediction accuracy, reduce delays, allow for better budgeting and unlock early risk detection, leading to greater certainty in the outcome of these projects.

Alastair Forbes, Network Rail 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."

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