nPlan on track for rail sector expansion with Transpennine Route Upgrade deal

1st November 2022 - London, UK: Tech company nPlan has agreed a deal to provide its AI forecasting and risk management services to Transpennine Route Upgrade (TRU), a multi-billion-pound programme of improvements which will transform the railway between York, Leeds, Huddersfield and Manchester. nPlan’s deal with TRU will allow the latter to analyse more project schedules, more frequently, and get more meaningful insights and accurate forecasts than it could through any other method of assurance; following a ramping up period TRU intends to use nPlan rather than quantitative schedule risk analysis (QSRA) across its entire programme of works.       

Unlike QSRA (the previous gold standard for forecasting and risk management in construction) nPlan’s outputs are generated by parsing large volumes of historical project data with AI. At the start of each engagement, nPlan gathers the customer’s past project schedules and uses an AI technique known as Deep Learning (DL) to build a model which reflects how the customer executes projects. Among other things this model captures how the different construction activities the customer undertakes play out in different contexts. Once the model has been trained, it can be ‘fed’ schedules for upcoming projects to generate a truly independent, data-driven and performance-based forecast of how the project as planned will play out, along with detailed information on the risk profile of every activity in the scheme. The outputs of the AI-led process can then be interrogated via nPlan’s powerful web-based platform, enabling customers to quickly and easily zero in on the riskiest activities, test the impact of various risk mitigation scenarios, visualise their project pathway and much more besides.  

A typical QSRA process depends on human inputs regarding how long activities will take and which project activities are risky - this determination is inevitably influenced by cognitive biases such as optimism bias, recency bias, and salience bias. By using historical data and AI, nPlan is able to nullify the effects of these biases to generate a realistic forecast and identify risks which would otherwise have remained hidden from the project team. QSRA is also an extremely resource-intensive process, requiring a lot of people to spend a lot of time to come up with a result. Previously, TRU was only able to perform QSRA on individual projects once per month, and on its whole programme every three to six months - but with its nPlan model trained, TRU is now able to trigger an analysis whenever it has an updated schedule, ensuring the project team can continue to reduce risk at the rate required to keep the project on track.

Richard Palczynski, Head of Strategic Programme Controls at TRU, commented: “The Transpennine Route Upgrade is an ambitious and complex multi-year programme. Getting the right forecasting and risk management capabilities in place will be critical to finishing on time, on budget, and with the minimum possible disruption to passengers. With nPlan as a delivery partner, we’re able to take a new approach to analysing our schedules - removing the human bias in favour of learning from actual data and historical performance - and driving efficiency into the process. Being able to get more frequent analysis done on a larger volume of schedules is a game-changer for us - we simply don’t have the resources to use QSRA to generate the insights the programme needs to stay on track.”

nPlan’s footprint in the rail industry has grown rapidly in the past two years, with the likes of Network Rail, HS2, SCS, Deutsche Bahn, and Rail Projects Victoria among the project owners and contractors making use of nPlan’s risk management and forecasting capabilities.

Dev Amratia, CEO of nPlan, commented: “For some time the rail industry has been signalling its frustration with established assurance methods, which simply can’t provide the level of confidence required by complex, multi-year rail programmes. Data-driven solutions like nPlan’s represent our best chance in a generation to get rail project execution back on track. The market seems to agree - we’re currently seeing very strong demand from rail project owners and contractors looking to upgrade from low quality, resource-intensive methods of providing assurance to an AI-led approach. We’re delighted to be partnering with the TRU programme team on this historic project - not least because they share our vision of delivering continuous forecasting and risk management for major rail programmes.”

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