Activity performance prediction
We use the largest database of as-planned and as-built construction projects in the world (>750,000 programme files) to train deep learning models to predict the performance of all activities in any construction programme. Our deep learning models use a variety of features that ensure each activity is uniquely described, and go through a rigorous testing process to ensure the highest accuracy.
Graph Neural Networks
Harnessing the power of GNNs, we analyze relational data among tasks, resources, and timelines to extract actionable insights. This relational understanding enables us to optimize resource allocation and uncover potential bottlenecks before they impact project timelines.
Advanced simulation engine
We saw the industry standard for Monte Carlo simulation and said "it's not good enough". We developed an in-house advanced simulation engine that works on programmes with tens of thousands of activities and can produce forecasts in minutes. This way, we can ensure that the forecast information you get is reflective of your actual programme, and not a summary.
Risk Analysis Algorithms
Because not all AI is done with Machine Learning, we also do research in automated risk analysis methods to provide the most relevant information for a risk professional to manage.
Forecast-driven schedule summaries
When working with schedules of tens of thousands of activities it is inevitable that the schedule will need to be summarised. We have developed schedule summarisation methods that work hand-in-hand with our forecasts. So even if the schedule is summarised, you won't lose any important information.
Optimal risk management strategies
Finding the right risk management strategy is one of the most complex tasks in mega-construction projects. With 10s of thousands of activities contributing to the potential delay of a project, which ones should you focus on first? nPlan has developed several state-of-the art ranking methods that show you what are the top activities to manage in the schedule.
