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

Project scheduling and earned value control for Python: CPM, PERT with Monte Carlo schedule risk, minimum-cost crashing, EVM and earned schedule. Validated against published reference values.

Instant Plan vs. Actual S-Curve generator from Primavera P6 XML exports. No installation or Excel required.

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

End-to-end residential construction estimating workflow including quantity takeoff, BOQ, contractor pricing, cost-loaded scheduling, Earned Value Management (EVM), commercial reporting, and project controls.

Standalone infrastructure mapping and digital transformation roadmap tool

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

Lightweight Excel/VBA planning engine for project scheduling, simulation, and controls.

Construction Project Coordination Platform

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,...

Project Controls & Performance Analytics Dashboard | Google Sheets & Power BI | Schedule Performance | Cost Control | Resource Analytics

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.

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.

Dashboard kontraktor untuk monitoring progres pekerjaan, deviasi proyek, produktivitas tim, dan evaluasi kinerja kontraktor secara terukur.

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

Sistem monitoring progress proyek perumahan untuk membantu Project Manager, Site Manager, dan Pengawas mengontrol target dan realisasi pekerjaan.

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...

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

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.