A planned outage is the highest-stakes schedule in industry: thousands of interdependent activities, a unit earning nothing while it's offline, and a return-to-service date the whole grid is counting on. We're building AI that plans it, watches it, and re-baselines it in real time.
A turnaround compresses years of deferred work into a fixed window. The unit is de-pressurised, cooled, opened, inspected — and only then does the real scope reveal itself. Findings on the boiler drive turbine work; a failed hydrotest ripples through every downstream trade.
Traditional planning tools treat this as a static Gantt. But an outage is a living system: hold-points, permits and clearances gate progress hour by hour, and the return-to-service date moves the instant one predecessor slips. Miss it and the cost is measured in millions per day — plus grid penalties.
Monte Carlo simulation over the outage network gives P10–P90 return-to-service dates and the live probability of hitting the target — updated as findings land.
Every clearance, isolation and permit modelled as a gate in the schedule — so the plan reflects what's actually cleared to start, not what was planned weeks ago.
When inspection reveals new work, agents propose an updated sequence and recovery options in minutes — grounded in CPM, with the RTS impact quantified before you commit.
Crew, scaffolding, crane and parts contention surfaced across boiler, turbine and HRSG work before two trades collide on the same platform.
We run Monte Carlo over the live outage network to produce a confidence band on the return-to-service date. As findings land and hold-points clear, the distribution tightens — and management sees the odds of hitting the target date update in real time.
This programme runs in partnership with an operating power utility and a university research group. Details disclosed on request.
We're looking for utilities, EPCs and researchers to pilot the engine on real turnaround data. Reach out to join the 2026 programme.