The world’s best project teams still fail to meet delivery expectations as they rely on the finite experience, unextractable bias and limited processing power of individuals. nPlan provides data-driven risk analysis and assurance for capital projects. Employing the power of machine learning on the world’s largest dataset of past construction projects, nPlan can objectively forecast delays and opportunities on the project, which improves outcomes and certainty of delivery.
nPlan’s data-driven approach shows that the human-planned finish date was unrealistic
During our workshop, we will help participants understand the manner in which data-driven tools can be used to forecast and estimate risk and uncertainty in project schedules. Attendees will be able to decide whether risk analysis is more accurate when employing Artificial Intelligence or the more traditional methods wholly reliant on human inputs and intelligence.
We will show how Machine Learning (ML), the branch of Artificial Intelligence best suited to quantitative risk analysis, can be used to quickly understand the risk in a schedule and provide insights on where to take action to mitigate this risk. Starting with the context for the project and an anonymised Primavera P6 schedule, we will split into teams and ask the audience to employ the outputs from the machine learning engine to identify impactful risks. We will then iterate the schedule for each team to see how much its virtual ‘mitigation’ has managed to reduce the overall project duration. Finally, attendees will be able to navigate to see the actual outcome of the project. Thus, the audience will be able to establish for itself whether Artificial or Human Intelligence is best placed to assure successful outcomes.