This follows the successful completion of a trial with the technology start-up where NR tested nPlan’s risk analysis and assurance solution on two of its largest rail projects - the Great Western Main Line and Salisbury - Exeter resignalling programmes, which are costing more than £3bn.
“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,” NR says. “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.”
NR hopes that using machine learning will allow it to transform the way projects are delivered. The nPlan algorithm learns by comparing what was planned against what actually happened at an individual activity level. The data can then be used to produce accurate cost and time forecasts for schemes to enable them to be planned and implemented more efficiently and avoid work over-running.
NR says that by using data from over 100,000 programmes, it “will increase prediction accuracy, reduce delays, allow for better budgeting and unlock early risk detection, leading to greater certainty in the outcome of these projects.”