BAM will employ nPlan’s newest product to improve risk management in an initial portfolio of 50 projects, while working closely with the company to guide the product’s roadmap
The launch partner for nPlan’s portfolio, BAM is the first contractor to use nPlan’s AI and big data to assist with scheduling risk management across its projects. nPlan’s advanced technology means that BAM’s capabilities will analyse a whole portfolio through an AI lens. This will provide unbiased insight into potential risks to schedule and cost, and enable prompt preventive action.
Applying nPlan’s machine learning ability at a portfolio level will help BAM focus attention on the right areas, to identify the efficiencies and opportunities through portfolio schedule optimisation. Ultimately this will enable more affordable outcomes for clients during a time of increasing cost pressures and funding constraints.
The value of forecasting
Since its founding in 2017, nPlan’s core contention has focused on big machining to be used for large-scale projects can only be effectively forecast through previous project data. nPlan set about assembling the world’s largest dataset of as-planned and as-built schedules from past projects, and used this data to train an AI to model project performance.
nPlan has long argued that the value of its innovative patented process is in enabling contractors and owner-operators to proactively manage risk to get the project outcome they want (rather than in passively adjusting contingencies or producing reports).
This argument has already won over project teams at the likes of Network Rail, Shell, HS2, TransPennine Route Upgrade, Kier, SCS and more.
Until recently, accurately quantifying portfolio delay risk was challenging. The human biases that undermine the accuracy of individual project schedules has a lasting impact on the accuracy of a portfolio-level analysis. nPlan Portfolio uses the same AI and big data-powered forecasting engine as nPlan Insights, nullifying the impact of bias at the project level.
Portfolio managers can now accurately quantify portfolio delay risk for the first time, and have unbiased data that represents which projects truly require attention.