The team proposed a comprehensive behavioural analytics dashboard to expose hidden patterns in project scheduling and resourcing that undermine delivery confidence. Using defined schedule and resource integrity metrics, the solution highlights chronic under‑ and over‑utilisation, forecast inaccuracy, ignored dependencies, and reliance on gen...

The team explored persona‑driven behavioural analytics to address risky resource planning practices. By combining detailed persona definitions, behavioural metrics, and deep analysis of forecasting and utilisation data, they designed a dashboard concept that highlights over‑optimistic planning, generic resource use, and weak feedback loops,...

The team developed a proactive behavioural analytics solution focused on surfacing risky resourcing and forecasting behaviours early. Using Python-based analysis and clear visual storytelling, they translate schedule and resource data into behavioural flags and practical recommendations that support early intervention and constructive, blame-fre...

TerraCast developed TerraCast, a machine‑learning based forecasting approach that combines data quality checks, classification, and regression models to predict schedule delay risk and likely lateness across energy projects, supported by dashboard‑ready outputs.

The team delivered a Power BI–based behavioural analytics solution that visualises forecast accuracy and resource utilisation to expose poor planning practices. By cleaning and transforming milestone and financial data, they created interactive dashboards that highlight generic resource use, under‑utilisation, over‑allocation, and forecast...

PRISM (Planning Risk Insight and Scheduling Monitor) is a working behavioural analytics solution that exposes risky resource and forecasting practices across portfolios. Built for Challenge 5, it provides persona‑specific dashboards for planners, resource managers, project managers, and senior leaders, analysing utilisation, forecast accuracy,...

FutureFlo delivered FutureFlo, a data‑quality‑led schedule forecasting solution that combines structured data cleansing, feature analysis and Power BI visualisation to highlight drivers of project slippage and forecast future delivery risk.

The team focused on establishing strong data quality and analytical foundations for a Project Health and Behaviour Monitor. Using a structured synthetic dataset, they demonstrated how task-level schedule, cost, and resource attributes can be cleaned, validated, and analysed to identify volatility, critical path risk, forecasting accuracy issues,...

Project Overrun Predictor built a machine‑learning driven schedule‑forecasting prototype that predicts the likelihood of project overruns by analysing feature trends across completed and in‑progress energy projects, supported by an interactive Streamlit application.