Parse the contents of a P6 .xer file into a Python object

Weekly Plan vs Actual dashboard for engineering/construction projects. Power BI (DAX/modeling), variance hotspots and a simple pace index. Demo data included.

This project aims to assess construction cost at completion, known as EAC, using artificial intelligence to take into account past data and provide accurate estimations

The team developed an automated Work Breakdown Structure generator that converts narrative project scope documents into structured, standardised schedules. Using defined activity standards and sample enterprise schedule data, the solution demonstrates how unstructured text can be transformed into consistent WBS elements with activities, duration...

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,...

Forecast Input Cost App delivered a Power Apps and Power BI based cost‑forecasting solution that enables controlled forecast entry, integrates actual spend data, and provides clear visibility of cost performance against estimates across projects.

WBS Cost Estimation Tool developed a desktop‑based Work Breakdown Structure (WBS) and Cost Breakdown Structure (CBS) estimation tool that supports structured cost entry, versioned change tracking, and comparison of estimates against actuals across the project lifecycle.

Integrated Cost Data Platform delivered an end‑to‑end cost management architecture that migrates fragmented Excel‑based cost data into a structured PostgreSQL database and exposes a single, trusted cost model to Power BI for analysis across estimation and execution.

Field-first AI framework for construction cost control — capturing execution reality before enforcing reporting structure.

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.

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 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...

ProCost built ProCost, a Power Apps and Power BI prototype for intelligent cost management that integrates actuals, forecasts and requisition data to provide scalable, modular cost visibility across the project lifecycle.

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.

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...

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...

MeridianIQ - The intelligence standard for project schedules. Open-source schedule intelligence from validation to prediction