Real-time EVM dashboards with AI-powered forecasting and early warning detection.
Progress Analytics is the executive view — every project, every metric, every risk surfaced together. Earned value indices update as field data flows in. Schedule slippage detected the day it happens, not the week the report runs. Cost trends extrapolated by the AI weeks before the variance becomes obvious.
For a portfolio manager running ten projects, this is the daily drive-by: which project needs attention today, which contractor is showing patterns of front-loading, which pay item is running ahead of plan and may indicate scope creep. For a single-project RE, it's the quick look at where things stand before the morning meeting.
All of it runs off the same data that drives the daily reports, the Quantity Book, and the schedule. No reconciliation, no spreadsheet exports, no week-old numbers.
Built for construction professionals — not generic SaaS.
PV, EV, AC, CPI, SPI per project + per WBS. CV and SV in dollars and percent. Trend lines over the project lifetime.
The AI watches for early indicators — CPI trending below 1.0 for three weeks, SPI consistently negative, change order velocity spiking — and surfaces them in the morning briefing before they're obvious in the numbers.
Estimate at Completion forecasts at three methods (CPI-weighted, schedule-weighted, blended). The AI explains which one is most reliable for this project type and stage.
Multi-project view for executives — health indicators per project, aggregate burn rate, contracted backlog, which projects are at-risk this week.
When a project misses its CPI target, the AI attributes the miss to specific contributors — which pay item, which contractor, which schedule activity. Not just "we're behind" — "we're behind because line 30300112 is over-running 2.3x."
Beyond the standard EVM set, build custom metrics — environmental compliance percentage, MBE participation, safety incident rate per labor hour. Same dashboard, same export.
Earned value calculations run on a continuous job that recomputes after every accepted daily report or change-order acceptance. CPI / SPI / EAC are stored as a time series so the dashboard shows trends, not just point-in-time snapshots.
Early-warning detection uses simple, transparent rules (rolling-window thresholds) over the time series — not a black-box model. When the system flags a project as at-risk, the user can see exactly which threshold was crossed and on what date.
Forecasting uses the three standard PMI EAC formulas plus a Monte Carlo confidence interval based on the project's historical CPI variance. The AI doesn't pick a number — it shows the range and the assumptions behind each method.